sayyad asghari

The danger of earthquakes causes irreparable damage to societies. Therefore, it is effective to prepare to deal with it by identifying vulnerable points. Ardabil City is not exempt from this rule due to the existence of many faults, seismic points, and non-compliance with standards. Based on this, the current research seeks to measure and evaluate the vulnerability of Ardabil City against the risk of earthquakes. For this purpose, first by examining research sources related to the subject, some of the most important factors affecting the occurrence of this phenomenon were prepared as independent variables. Valuation and standardization of the layers were done by using the fuzzy membership function and weighting of the criteria, using the CRITIC method. Finally, modeling was done using the MARCOS multi-criteria decision-making method. The results of the study showed, respectively; The factors of distance from the fault, land use, and distance from the earthquake center have the greatest influence on the occurrence of earthquakes in the region with the weight coefficient of 0.110, 0.104, and 0.102. In addition, according to the results of the research, 16.67% of the entire city of Ardabil has a high vulnerability potential and 30.27% has a relatively high vulnerability probability. Also, based on the results of the research, Ardabil city is at an average level in terms of the level of vulnerability to earthquake risk, the cities of Hir and Arallo are in the high vulnerability category and Somarin city is in the relatively high vulnerability zone. In addition, according to the findings of the research, 22.86 and 30.86 percent of the settlements and villages of Ardabil, respectively; In the area with high and relatively high vulnerability, 13.14% in the area with moderate vulnerability, and respectively; 22.28 and 10.86% are located in the area with relatively low and low earthquake vulnerability.

The continuation and stability of the river catchments depends on the passage of water and the transfer of sediment. Just as river flow information is vital for many practical applications such as water allocation, long-term planning, catchment management operations, flood forecasting, design of hydraulic structures, etc., amount of sediment yield in rivers is important from a variety of aspects such as river morphology, engineering designs to river water resource, reservoir dams, river organization to Inhibition of erosion and floods, irrigation networks, etc. are important. Proper and sustainable use of water and soil resources within the river catchments requires awareness of the spatial variations of hydrogeomorphic elements (water discharge, sediment discharge) and the determinants of these variations. Various studies refer to the key role of forests in the water cycle, soil protection and habitat protection, and show that increasing or decreasing forest cover has had an effective role in the hydrogeomorphic regime of the rivers. Due to the importance of interdisciplinary studies and understanding of interactions between different biological and non-biological components of river catchments in regards to sustainability and continuity of natural and human environment, the present study intends to be aware of the quality and quantity of relationships between forest cover and discharge/sediment yield in the Talesh catchments, NW. Iran.

This study was based on correlation and regression analysis of the ecohydrological relationships. The research statistical population included the Talesh region and the research sample population included 12 important catchments in this region. The data used included monthly water discharge and sediment yield data of hydrometric stations on the one hand and the digital satellite images included the Aster digital elevation model (DEM) and Landsat 8 image on the other hand. Data analysis tools include geographical information system (GIS), Google Earth, SPSS and Excel. The steps of conducting the research were such that: firstly, the hydrogeomorphic variables including the mean, maximum and minimum rates of water discharge and sediment yield (S.Y), were calculated for 2020yr. Then, the 12 watersheds were extracted based on the position of the hydrometric stations from the DEM with a spatial resolution of 30 meters. The next step was to extract the forest cover from the Landsat 8 image, which was obtained on June 3, 2022. In this regard, the satellite image classification in the form of two class of forest and non- forest was through the maximum likelihood algorithm. Satellite images were classified using 300 training points. Finally, the correlation test between the forest cover and water discharge and sediment yield was performed in the SPSS software. The significant level of correlation relationships was ≤0.05.

The results of the correlation test showed that there was a significant relationship between the percentage of forest cover and the water discharge in the catchments. The correlation coefficients (R) were -0.58, 0.58, -0.46, respectively for mean, maximum and minimum discharges, indicating that definite and positive role of the forest in preventing rapid and erosive runoff. In contrast, the relationship between the percentage of forest cover and S.Y in the studied catchments was not significant and the correlation coefficients for mean, maximum and minimum S.Y are at -0.07, -0.05, -0.06 respectively, which is indicative of the weak correlational relationship between the two variables. However, the separation of catchments as large catchments and small led to improvement of correlation relationship between forest cover and S.Y, despite being insignificant, so that correlation coefficients in small basins for mean, maximum and minimal S.Y, respectively of -0.656, -0.60, 0.339, respectively. Given the significant relationships, it was possible to provide regression prediction equations for monthly mean and maximum discharge based on the percentage forest cover in the catchments. The insignificant relationships between forest cover and the sediment load can be explained by complex nature of the sediment load transfer, especially storage and re-movement of sediment load, making it difficult to establish significant relationships between the sediment yield (S.Y) and the environmental variables affecting it. On the other hand, the relationship between vegetation and the S.Y in catchments is usually complex and nonlinear, and therefore the use of nonlinear relationships may make the relationship more complete and obvious. Finally, the multiplication of the factors involved in the production and transferring of sediment load makes it difficult to model and predict the spatial variations of sediment load.

The present study attempted to provide a quantitative analysis of the spatial relationship between the forest cover and the rates of water discharge and sediment yield (S.Y) in the Talesh catchments by adopting an interdisciplinary approach. The results of correlation analysis show that the presence of significant and inverse relationships between the percentage of forest cover and water discharge of the catchments reflects the distinct role of the forest in reducing runoff and the incidence of dangerous floods in the Talesh catchments. In contrast, the relationships between forest cover and S.Y in the studied catchments are not significant. This conclusion shows that we are not able to provide a prediction model of S.Y variations based on the percentage of forest cover in the catchments, but the reverse relationship between the percentage of forest cover and the S.Y of the catchments implies the protective role of the forest in adjusting the process of precipitation, runoff- erosion and enhancing of the environmental quality in catchment systems. First of all, it should be noted that the forest cover variable is considered as a modulator variable or interface between the inlets and outputs of the catchments system, so it is likely that the direct role of the forest on runoff and sedimentation couldn’t be easily grasped by statistics analysis. In this context, there are various natural and human factors that may play a more effective role in the S.Y variation of the catchments than the factor of forest cover. Increasing of correlation coefficients (R) between the percentage of forest cover and S.Y in small catchments indicate that these catchments are an ideal scale for identifying controlling factors of sediment yield.

Land displacement is known as one of the most obvious invisible effects of natural earthquake hazards. In the present study, the radar interferometric technique (SBAS) using the images of Sentinel 1 (2018 to 2021) was used to estimate the land subsidence, and the Landsat 8 image of 2018 was used to extract the land use classes in a part of the Sarab county. Also, the data obtained from the regional water organization has been used to investigate the condition of underground water in connection with the subsidence caused by displacement. According to the findings and survey of the land use map of the study area, the subsidence can be seen in different places and it reaches 9 cm per year around the agricultural areas and pastures. Also, in some places, the rising rate is estimated up to 12 cm. Examining the condition of aquifers shows a drop in the level of underground water in most stations, especially a sudden drop since the beginning of 2018. Considering that the direct relationship between groundwater withdrawal and subsidence has been fully proven; But after the 5.9 magnitude earthquake in November 2018, sudden changes have occurred in the subsidence of the region. Therefore, it seems that the subsidence of the region is more affected by the earthquake.

Soil erosion is a global risk that seriously threatens water and soil resources, and land changes, especially the destruction and change of forests, are important factors involved in increasing the potential of its occurrence. Therefore, planners and managers must be aware of forest cover changes and their role in causing soil erosion over some time. The evidence shows that with the increase in population and to meet the needs of the residents, the extent of forest lands in the Ghaleroodkhan basin has decreased and the amount of soil erosion has increased. Based on this, the current research seeks to evaluate the effect of changes in forest cover on soil erosion in the Ghaleroodkhan Fuman basin.

In this research, to investigate land use changes (especially forest), Landsat satellite images from (OLI-TM) sensors for the years (1992-2023) were obtained from the American Geology website in August. Then, to prepare the images, geometric and atmospheric corrections were made on the images using the Flash method and Envi5.3 software. In the next step, using the object-oriented classification method and the nearest neighbor algorithm by Ecognition software, land use maps were extracted in 1992 and 2023. In the next step, by identifying the effective factors involved in the erosion of the region (including; land use, slope, lithology, soil, distance from the road, distance from the river, and rainfall) and preparing the information layers of each criterion in GIS, valuing and standardizing the layers using Fuzzy membership function and criteria weighting were done using Critique method. Eventually; The final analysis and modeling was done using MABAC multi-criteria analysis method.

It can be said that the largest area of use in 1992 is related to forest cover with an area of 222.17 square kilometers, which has decreased to 205.03 square kilometers in 2023. The next land use, which has the largest area in the Ghaleroodkhan basin, is the use of agricultural land. The amount of agricultural area in 1992 was about 161.03 square kilometers, which decreased by about 11 square kilometers in 2023 and reached 150.05 square kilometers. The land use of residential areas has changed the most in the last 30 years, in the starting year (1992) was about 33.01 square kilometers, and in 2023 it reached the area of 60.18 square kilometers.According to the obtained results, the criteria of slope, land use, and lithology, respectively; In 1992, with weight coefficients of 0.164, 0.154, and 0.145, and in 2023, with weight values of 0.169, 0.158, and 0.142, they received the highest value of weight coefficient in both studied periods. . Also, according to the erosion zoning maps of the studied basin, it can be seen that in 1992, the area of the layer with very high and high erosion potential was 98.92 and 118.19 square kilometers, respectively. in 2023 respectively; 132.39 and 119.94 square kilometers of the basin surface have very high erosion potential.

The results of the study show a decrease in the area of forest cover in the Ghaleroodkhan basin during the study period. So about 1700 hectares of the forest lands of the basin have been reduced and converted to other uses. Mainly, a large area of the forested lands of the basin has been converted into non-forested lands such as agriculture, residential, and pastures, which indicates the not very suitable situation for protecting the region.It can be acknowledged that according to the trend of land use changes during the studied period, the reduction of forest cover and its conversion to agricultural areas, pastures, and residential lands, as well as encroachment on the privacy and river bed in the form of changes in the use of the river to agriculture and residential. Along with other natural factors of the range, such as; The presence of sensitive and erodible formations, high slope, adequate rainfall, and the abundance of waterways have played the greatest role in increasing the soil erosion potential of the Ghaleroodkhan basin. Due to the severity of destruction in the study area, necessary management measures should be taken as soon as possible to prevent and reduce further damage to the forests of the area, as well as to reduce erosion. Eventually; It should be acknowledged that the use of the land use change map and the erosion risk zoning map obtained in the present study is a practical guide to know the state of forest destruction in the region and the erosion potential of the basin.

Remote sensing data has played an important role in natural resource management studies in recent years. These data, especially in water resources studies and research, have many uses. Among water-related studies, the use of water indexes in recent years has been widely considered. These indexes have grown and developed with the advancement and production of satellite images and their precision increased dramatically. In this research, Landsat 8 and Sentinel A2 satellite images were used on the coast of Bushehr on the Persian Gulf. 8 water indexes were selected and executed on images. Despite the fact to exist two classes of water and land unsupervised classification were applied to images Finally, the overall accuracy and kappa coefficient values range from 77.0% to 99.6% and 0.55 to 0.99 respectively. For Landsat images, the Modified Normalized Difference Water Index (MNDWI) and the Normalized Difference Pond Index (NDPI) were the best indexes. Water Ratio Index (WRI) and Normalized Difference Turbidity Index (NDTI) were recognized as the worst index. For Sentinel 2A images, the Modified Normalized Difference Water Index (MNDWI) and the Normalized Water Difference Index (NDWI), respectively, were the best. and the Automatic Water Extraction Index (AWEI_NSH) had the worst result. In general, the performance of the water indexes, and the accuracy level of the sentinel 2A images was significantly higher than the Landsat 8 images This factor can be due to the higher spatial resolution of Sentinel images. For both Landsat 8 and Sentinel A2 images the Modified Normalized Difference Water Index (MNDWI) has the best results.

Sediment that consists of solid particles with organic matter transported by water is called suspended sediment. In other words, the sediment load is the flow of the total sediment output from the catchment or drainage basin that can be measured in the desired cross-section and in a certain period of time. In most natural rivers, most of the sediments are transported as suspended load. The sediments collected by the rivers cause many problems, including sedimentation in the reservoirs of dams and reducing their useful volume, changing the course of the river due to sedimentation in their bed, reducing the capacity of canals and water transfer facilities, and changing the quality of water in terms of drinking and agriculture. . However, no direct or indirect experimental model developed to evaluate this process has been universally accepted. Therefore, sediment transport has been considered by engineers from different aspects and different methods have been used to estimate it. One of the methods of estimating suspended sediment is artificial neural network. Artificial neural network is a computing mechanism that is able to provide a series of new information by taking information and calculating it. Considering that the structure of the human brain has a very high ability to process complex, non-linear and parallel information.

As one of the sub-basins of the Aras catchment basin, Gharasu catchment is located in the geographical coordinates of 47°31' to 48°47' east longitude and 37°47' to 38°52' north latitude.In this study, the statistics and information of 17 variables in 13 sub-basins of the Gharasu River, which were extracted by the regional water organization of Ardabil province, were obtained from this organization. In order to model the artificial neural network from the data of 3834 daily sediments recorded in 13 sediment measuring stations in the studied sub-basins during a 50-year statistical period corresponding to the statistical period of 1350 to 1399 and also from the digital topographic maps of the 1:25000 scale of the Geographical Organization of the Armed Forces to Validation of basin demarcation was used. In choosing this common time base, criteria of completeness, sufficient length of data and use of the latest available data were taken into consideration. Then the normality and correlation between the obtained data were evaluated and two methods of Radius Axis Function (RBF) and Multilayer Perceptron (MLP) were used in SPSS software to model the artificial neural network.

recorded suspended sediment (3834 cases) in the relevant statistical period was considered as a dependent variable and flow rate as an independent variable separately for each sub-basin, and Pearson's correlation method was used to check the correlation between the independent variable and the dependent variable.According to the correlation matrix of the variables, it can be seen that Barouk sub-basin has the highest correlation and Arrab Kendi and Pol Almas sub-basins have the lowest correlation. After modeling the data by two artificial neural network models (RBF and MLP), the amount of sediment for each year was predicted by these models and R2 and RMSE values were also calculated for them. In order to determine the number of neurons in the hidden layer, the values of the neurons in this layer were evaluated by trial and error, and according to the results, choosing the number of 4 neurons for the RBF model and 3 neurons for the MLP model has the highest prediction accuracy in the evaluation data and It also shows in the test data. The accuracy of prediction in RBF model with correlation coefficient R2=0.941 and RMSE=65.002 is compared to MLP model with R2=0.917 and RMSE=88.244.Based on the scatter diagram between the real data and the estimated data, it was determined that the average of the real values is 4.636, which is 4.367 for the RBF model and 3.534 for the LMP model, which indicates better accuracy in Modeling and the closeness of the RBF model value to the real value. Regarding the median index and the mode index, which represent the most repeated data in the statistical collection, for the real values, the numbers are 4.117 and 3.246, respectively, and for the RBF model, the numbers are 4.425 and 4.213, respectively, which are the closest values. It is considered as a real amount.

So far, various forecasting models have been used to estimate river sedimentation. Some of these models have estimated the amount of sediment by combining different physical parameters of the basin, climate and even the output of satellite images. Artificial neural network models are widely used in forecasting geographic models today.In this research, two artificial neural network models, radial axis function (RBF) and multi-layer perceptron (MLP) in SPSS software have been used to estimate the sediment of Gharasu River in Ardabil province. In this study, recorded suspended sediment (3834 cases) in a 50-year statistical period was considered as a dependent variable and flow rate as an independent variable separately for each sub-basin, and Pearson's correlation method was used to check the correlation between the independent variable and the dependent variable. It was found that Barouk sub-basin had the highest correlation and Arbab Kendi and Pol Almas sub-basins had the lowest correlation. After modeling the data by artificial neural network model, the amount of sediment for each year was predicted by these models and R2 and RMSE values were also calculated for them. The prediction accuracy of RBF model with correlation coefficient R2=0.941 and RMSE=65.002 is higher than MLP model with R2=0.917 and RMSE=88.244, and it has a better performance in estimating suspended sediment in the study basin.Also, the average value of the real values is equal to 4.636, which is equal to 4.367 for the RBF model. This research showed that in all studied stations, the RBF method provides more accurate estimates of suspended sediment than the MLP model. Of course, due to the existence of complex relationships between flow rate and suspended sediment, the appropriate model should be determined in each hydrometric station to estimate this variable more accurately,

One of the important conditions for optimal use of land is obtaining information about landuse patterns and their changes over time. Landuse is usually defined based on human use of land, emphasizing the role of land in economic activities. Today, remote sensing technology is considered as the main element in landuse monitoring. The aim of the current research is to extract landuse maps for the years 2000 and 2021 in FirozabadKhalkhal region and to investigate the changes made in the studied time period in the region using the images of ETM and OLI sensors of Landsat. Also, checking the capability of basic pixel and object-oriented methods for landuse classification is another purpose of this study. In the current research, the object-oriented technique nearest neighbor algorithm and the vector machine method supporting the pixel-based algorithm have been used for landuse classification. Then, to verify the accuracy of these two methods, the overall accuracy and Kappa were extracted. The results of this evaluation show the high accuracy of the object-oriented method in extracting land use classes. Based on the results of the detection of landuse changes in the studied time period, the highest amount of changes occurred is related to the use of good pasture to poor pasture with a value of 51.72 square kilometers, followed by forest to good pasture with a value of 30.11 and the lowest changes It is related to the use of pasture and water with the amount of 0.03 square kilometers. The reasons for these changes are the increase in population, indiscriminate grazing of livestock, incorrect and illegal use of different lands. The use of more parameters such as scale, shape, compactness, color, texture, smoothness criterion and pattern for landuse classification in the object oriented technique can be considered as an innovation of the present study.

Land subsidence is one of the ecological crises that especially cause damage to human facilities such as communication networks and buildings. Many factors affect the subsidence of the earth, and earthquakes can be one of those factors. The current research examines the effect of the November 2017 earthquake in the western region of Kermanshah on subsidence. For this purpose, the differential radar interferometric method and Sentinel 1 satellite data have been used to investigate the subsidence of the region. Pair processing of radar images in the study area between 11/11/2017 and 11/30/2017 showed that the amount of vertical displacement in the study areas is between -41.72 and -42.99 cm. According to the obtained results, the average displacement of subsidence in the opposite direction of view is -304.16 and -33.1265 cm, which was observed along the fault lines. Also, the average uplift movement rate in the studied area is 17.53 and 34.53 cm per year in line with the satellite view. The evaluation of the displacement map of the region shows that the greatest amount of subsidence was in the eastern, southern, and western parts of Salas Babaji City and the southern and western parts of Sarpol-Zahab city.

The changes in the river flow regime are the main factor in the stability of the river ecosystem and may change due to factors such as dam construction, water abstraction, flow diversion, and climate change. The hydrological regime of the river is the main driving force of ecosystem dynamics and river functioning. In addition, hydrological regimes play a major role in changing the structure and ecological processes of river ecosystems. On the other hand, the morphological characteristics are constantly changing over time as a dynamic system and these changes will be different due to changes in river flow and sediment transport capacity. The management of the watershed according to the distribution of the annual values of each of the 33 parameters of hydrological changes within the range of natural changes of the parameters is the basis of the range of variability approach. When the purpose of river flow change analysis is different between two time periods, the IHA software allows users to use RVA change methods to calculate indicators and compare results. The effective management of the river ecosystem requires the description of the parameters of the hydrological regime of the natural flow and the determination of the degree of changes in the flow rate of the regulated flow compared to the natural flow. Based on the literature review, the assessment of changes in the regime of rivers in mountainous areas requires comprehensive research. Khiavchai River of Meshgin shahr is a snow-fed rivers and a typical mountainous river, which has been selected as the study area. The purpose of this study is to investigate the changes in hydrological flow indicators in Khiavchai River, in this regard, long-term discharge data and range of variability (RVA) are used.

First, the changes in annual discharge statistics were evaluated in the periods of 1969 to 2019 and then the studied periods were analyzed based on the change point analysis software. The range of variability approach is a type of hydrologically-based methods for river flow analysis. In this regard, management goals should be determined based on available ecological information regarding the river flow regime. In the absence of appropriate ecological information, it is recommended that the standard deviation range can be considered as a default for the initial determination of targets. In other words, the normal values of each of the IHA parameters in the normal state should be considered within the range of standard deviation (± STD) from the average values, or the 25% and 75% quartiles should be considered as the lower and upper limits of the parameters, respectively. The results of the changes in five groups of parameters of hydrological changes (IHA) in the range of range of variability changes were obtained at the Pol-Soltani hydrometric station, Khiavchai River using IHA software. In this regard, hydrologic change indicators are calculated using Indicator of Hydrologic Alteration software and compared and analyzed in different periods.

The results showed that the annual runoff series changes in two consequent periods. The changes in the average discharge variables in different months in both study periods of the year have been decreasing and the rate of change of the index has been negative compared to the previous periods. The results showed that the low flow discharge minimum 1, 3, 7, 30 and 90 days decreased and decreased to zero in the third period compared to the second period, to a value of 0.01. The mean changes of the base flow index also showed a decrease from 0.02 to zero. It is worth noting that the base flow index can lead to the reduction of snow storage or the destruction of vegetation, and as a result, it is upstream of the study area. The increasing trend of low flow pulses and recession rate also decreases considerably.

As a concluding remark, the pattern of changes in river flow indicators based on the changes of hydrologic indices in both study periods had a similar trend, which indicates the decreasing trend of changes in river flow regime components, and these changes have occurred as a result of water abstraction and river flow diversion. Although a part of the changes in discharge values can be related to the change in hydroclimatic variables, but due to lack of sufficient studies on changes in climatic variables, it is not possible to draw a firm conclusion in this regard. Determining changes in climate generators for the river flow regime can be suggested as one of influencing factors in river behavior changes as well as better management of surface water

The purpose of this study was to investigate the effect of physical and chemical properties of soil on the creation and spread of gully erosion in the region and by providing appropriate solutions to prevent the spread of this type of erosion in the region. In order to identify the natural features of the basin and collect theoretical issues, the basic topographic maps (scale 1: 25000) of the National Mapping Organization and the geological map (scale 1: 100000) of the Geological Survey of Iran were used. Digital elevation model (DEM) and land use map were extracted from Landsat 8 images in 30 by 30 dimensions. The mentioned maps were prepared in ArcGIS10.5 environment. The mentioned maps were prepared in ArcGIS10.5 environment. Then, with field visits, 15 gully were selected and their location was recorded by GPS, and then soil samples were taken from each gully from a depth of 0-30 cm. In the continuation of the work, 5 points were selected from non-gully areas as areas that were not affected by ditch erosion and their location was recorded and soil samples were taken from a depth of 0-30 cm. Soil samples collected from the basin were examined in the laboratory for physical and chemical properties. Then R software was used for statistical analysis. The results showed that there was no discrepancy between the studied variables in the two groups (A: gully areas, B: non-gullyareas). The results of physical properties showed that the normality of sand data in gully and non-gully areas is confirmed (0.880) and (0.484), respectively. Also, the value of leven test (0.499) expresses the homogeneity of variance between the data of the two groups. Finally, sand was able to show a significant difference between the two areas (0.001).

The start of the Quaternary period, along with all geologists and geomorphologists and other geoscientists, has been accompanied by climatic changes, in other words, the spread of glaciers in high latitudes and high ground elevations. The glaciers and glaciers of the Quaternary Mountains have eroded the lake of these areas and created numerous geophysics at different altitude levels. In the first half of the nineteenth century, Swiss geologist Luigi Agassis studied the role of natural glaciers in the evolution of the landscape. After that, glaciers were considered as one of the processes in the formation of geophysics in geomorphology. Continuous assessment of glaciers is important in terms of global warming as well as water supply downstream. The evolution and variety of geomorphic forms of the Earth's surface is affected by various morphogenesis processes that have been active over time and have created distinct landscapes.

In this study, in order to reconstruct and estimate the snowboard of the last Quaternary glacier period in the northern slope of Sabalan Mountain, Wright and Porter Methods (altitude of circus floor and altitude proportions) were used and past morphoclimatic condition based on geomorphologic evidence and temperature and rainfall of the Glacier Vorm period (using climatic evidence and climatic condition) was estimated. To better identify and reconstruct the glacier forms in the last Quaternary glacier period at the studied area, a combination of topography maps, field observations, satellite imagery, Digital Elevational Model (DEM), images received from Google Earth and climatic data was used. By identifying 25 circuses at different altitudes and based on the mentioned methods, the permanent snow line of the studied area was determined in the last the Quaternary glacier period. The basis of the Wright method is to determine the altitude that 60% of the circuses are above it. In order to estimate the temperature conditions and to prepare the companion map of the studied area, the regression equation between temperature and elevation was calculated in Excel software. Using this relationship and the raster computation function, a homogeneity map was prepared. The regression equation between precipitation and elevation was calculated in Excel software and then, using the raster computational function, a map of the plot was prepared.

The permanent flood line of the last Quaternary glacier (Vorm) in Northern slopes of Sabalan was 3242 meters in Wright method, the altitude ratio method was 3500 meters and the circus floor height was 3473 meters. According to Wright’s method, height of the permanent snow line in the last glacial period was 3869 meters and according to Porter’s methods (altitude of circus floor and altitude proportions) 3749.32 and 4017.5 meters, respectively. After calculating the height of the Quaternary permanent snow line in Sabalan Mountain by Wright’s method (3769 m) using Digital Elevational Model, the study area was divided into two parts: areas covered by permanent snow and adjacent areas of glacier or preglacial. After calculating the height of the Quaternary permanent snow line in Sabalan Mountain by Porter’s methods (altitude of circus floor 3749.32 m and altitude proportions 4017.5 m) using DEM, the study area was divided into two parts: areas under permanent snow control and areas outside permanent snow control. To prepare the coherent map, regression equation was calculated between temperature and altitude. The height of 4066 m was estimated as the snow line of the current permanent boundary of Mountain. Reconstruction of the temperature conditions of the study area in the last Quaternary glacier period, the average temperature was estimated to be 3.68 ° C cooler than the present. Also, the regression equation was calculated between precipitation and height in order to study the rainfall and moisture conditions of the study area. With regard to the current snow depth of the studied area, rainfall is reduced by 26 mm compared to Vorm.

The Wright and Porter methods estimate the snowfall of the Northern slopes of Sabalan in the Quaternary, 3242, 3473, and 3500 meters, respectively. Comparison of the results obtained for the height of the current snowboard line shows that the current snowboard at study area is 197, 48.5 and 317 m higher than the last Quaternary glacier period calculated by Wright and Porter methods (altitude of circus floor and altitude proportions), respectively. The necessary condition for the formation of glaciers is 0° C and an increase in precipitation compared to the current. According to the estimated snowfall height of the current in study area (4066 m), the current precipitation decreases by 26 mm compared to Vorm and also, the past temperature was estimated to be 3.68° C colder than the current. Therefore, increasing rainfall in the studt area (26 mm) in the past along with decreasing its temperature (3.68° C) compared to the present can prove the existence of Sabalan Mountain glaciers in the quaternary era.

The earthquake is one of the most important environmental hazards in many Iranian cities. In this regard, spatial assessment and micro-zoning of seismic vulnerability of cities are essential requirements in earthquake mitigation planning. In the present study, seismic vulnerability of Kermanshah metropolis is investigated. Crossing three important fault lines in the vicinity of Kermanshah city has caused earthquake hazard in this urban area. One of the main measures to reduce the vulnerability of Kermanshah city to earthquake risk is seismic assessment and zoning of the city in order to implement earthquake mitigation plans. In this context, 11 variables affecting the seismic vulnerability of the city were used. These variables are: fault distance, slope, lithology, population density, land use, residential density, and major communication networks, access to hospitals, operating radius of fire stations, and distance from hazardous facilities, and access to urban open spaces. Fuzzy TOPSIS technique was used to combine these variables in the context of GIS. The results show that about 32% of the area of Kermanshah is in a very high vulnerability class. Neighborhoods located in the central part of the city (the core of the city) are among the most vulnerable to earthquake hazards in Kermanshah. In this context, factors such as proximity to faults in the southern margin of the city, high population density, high residential density, marginalization, the presence of worn urban texture, and the lack or severe shortage of open urban spaces are among the most important causes of high vulnerability of these neighborhoods.

gully erosion is one of the most important land degradation processes that leads to reduced land use. The southern and southeastern parts of the Shoor -Mohr basin have been affected by gully erosion. The consequences of this type of erosion include disturbing the ecological balance of the region, endangering biological resources, which has necessitated the study of gully erosion in the region. The purpose of this study is to prepare a zoning map for gully erosion sensitivity using the maximum entropy model and GIS. To achieve this goal, 15 variables including height, slope, slope direction, distance from waterway, drainage density, distance from road, soil texture, geology, land use, rainfall, distance from fault, vegetation, cross section curvature, flow strength index (SPI), Topographic Moisture Index (TWI) was selected and linearity test was performed using (Variance Inflation Factor) and (Tolernace) indices. Out of 100 selected gullys, 70% were randomly selected for educational data and 30% for classification validation and Bootstrap sampling method was used to run the model. Jackknife test was used to determine the most important variables and ROC curve was used to determine the predictive power of the model. Test results showed alignment between vegetation, fault distance, cross-sectional curvature, SPI and TWI were thus omitted. According to Jack Knife test, the variables of height, average annual rainfall, soil texture, drainage density, geology, distance from the road, distance from the river had the greatest effect on gully erosion, respectively. The ROC curve represents 95% accuracy in the validation stage of the model. According to this model, more than 15% of the basin (8445.83 hectares) is very sensitive to gully erosion.

Estimation of subsidence rate using radar interferometry technique and groundwater parameters and land use (Case study: Ardabil plain)IntroductionThe phenomenon of subsidence has affected many parts of the world, including Iran, and in recent years has been raised as one of the main issues and challenges. This phenomenon has developed in different regions under the influence of different factors. In Iranian subsidence, groundwater loss has been suggested as the main factor. Because there is a direct relationship between subsidence of areas and the rate of groundwater loss in different areas. In fact, the prevailing dry climate in most parts of Iran and the concentration of industrial, agricultural and drinking water consumption on groundwater resources, has provided a good infrastructure for the occurrence of this phenomenon. Due to the importance of the issue in recent years, various studies have been conducted in the field of subsidence and advances in the field of remote sensing have led to monitoring the phenomenon of subsidence with greater accuracy and speed than in the past. . Which has received a lot of attention in recent years Radar interference method that has high accuracy and speed in processing information and monitoring land surface changes. Therefore, in this study, this method has been used to monitor the subsidence of Ardabil plain.MethodologyIn this study, First, radar interferometry method has been used to investigate and measure the amount of subsidence. One of the powerful tools for monitoring the subsidence phenomenon is the radar interferometry method. This method is able to determine the changes in the earth's surface in that time period by comparing the phases of two radar images taken from an area at two different times. Sentinel 1 satellite images from 2016 and 2020 were used for this purpose. Considering the direct effect of groundwater decline and increasing its utilization rate on subsidence, in the present study, the status of piezometric wells in the Ardabil plain was evaluated by intriguing method (kriging). Kriging interpolation requires zero mean estimation error. Absolute estimation in interpolation is one of the main features of Kriging model. Also, This means that the estimated value of the quantity at the sampling points is equal to the measured value and the variance of the estimate is zero. Object-oriented method was used to classify the images in the land use map in this study. This is because the method uses information from other pixels (size, shape, texture, etc.) for classification in addition to spectral information. Image objects are created based on parameters such as scale, object shape, color, compression ratio that are determined during interpretation.ResultsThe study of subsidence according to Figure 3 in the last 5 years from 2016 to 2020 in the study area shows that the amount of subsidence is 4 mm per year and 22 mm during 5 years. In order to reconcile the results of radar interferometry with other supplementary data, land use map as well as piezometric wells in Ardabil plain were used. By reviewing and analyzing the land use map and subsidence of Ardabil and field studies confirm the maximum occurrence of subsidence in agricultural areas and good rangelands or 0.22 mm, medium rangelands 0.21 mm and 0.20 mm It provides unsafe and risky living conditions for the residents of Ardabil plain and indirectly has reduced the quality of environmental conditions and life of the residents of Ardabil plain. Land subsidence is a pervasive phenomenon in the world, which has had a significant quantitative and qualitative manifestation in recent years, mainly due to the improper exploitation of groundwater resources and the intensification of its surface decline. To investigate the situation and the effect of groundwater level decline and its effect on the subsidence of Ardabil plain from the information of 39 piezometric wells (obtained from Ardabil Regional Water Organization) during the years 1987 to 2020 that the results of the analysis of a decrease of 48% Shows the water level in 2020 compared to 1987. The maximum water level of piezometric wells has increased from 45 meters to more than 70 meters in 2020, which indicates the deterioration of the Ardabil plain aquifer. Which requires the identification of effective factors, mitigation measures and adaptation measures, including the restriction or prohibition of groundwater extraction in areas subject to subsidence and the implementation of strict regulations for extractors of groundwater resources in areas subject to subsidence and continuous monitoring Indicators of subsidence.ConclusionsIn this study, the extent and distribution of subsidence in Ardabil plain and the effect of groundwater level decline and land use on land subsidence were investigated by radar interferometry, interpolation and object-oriented methods. Studies show subsidence of 22 mm over a period of 5 years. The results of data analysis of 39 piezometric wells in Ardabil plain in a period of 33 years show an average groundwater drop of about 9.5 meters. Co-institutionalization of subsidence maps with land use layers also confirms the maximum occurrence of the settlement area, which has the highest rate related to good pastures and agricultural areas0 / 22. Mm, medium rangelands 0/21 mm and man-made areas0 /20 mm.

Gully erosion as one of the common forms of erosion causes deformation of the land surface and its spread is one of the main reasons for the destruction of agricultural lands, communication roads and irrigation system of the basin, especially the plains on the slopes east of the basin to Chaho village. The area is eroded and made inaccessible through the creation and development of ditches and the agricultural productivity of the area is reduced. Therefore, by studing the physical and chemical properties of the soil and also determining the topographic threshold, an attempt has been made to identify the factors affecting gully erosion and to prevent the damages caused by it as much as possible with a more accurate prediction.

The study area is the Shoor River watershed in the East and Northeast of Mohr city, which is located in the south of Fars province with an area of 101350 hectares. This watershed is located between 27 degrees, 27 minutes and 22 seconds to 27 degrees, 49 minutes and 41 seconds North latitude and 52 degrees, 24 minutes and 58 seconds to 52 degrees, 59 minutes and 14 seconds East longitude. According to the divisions of the country's catchments, the study area is part of the catchments of the Shoor and Mehran rivers, which eventually enters the Persian Gulf.In order to initially identify the natural features of the basin and collect theoretical topics, basic topographic maps (scale 1: 25000) of the National Mapping Organization and geological map (scale 1: 100000), Geological Survey and Mineral Exploration Organization were used. ArcMap version 10.3 software was used to draw the maps. Then 15 ditches were randomly selected. To select the topographic threshold, all selected ditches in the relation were used. While to study the physical and chemical properties of soil (sand, clay, silt, PH, EC, TNV, Mg, Ca, K, Ca, SAR, Na) and geometric measurements, gullys based on cluster analysis It was classified in which geometric measurements were considered as dependent variables and soil physical and chemical properties were considered as independent variables. Tests were performed on the data in SPSS software.

The results of clustering analysis showed that the gullys were classified into three groups. Since more than 53% of gullys are in group one, they have been used for statistical analysis. Correlation and multiple regression models were performed to analyze the relationship between dependent and independent variables. The results of Pearson correlation showed that the length and width of gullys had a very strong relationship with the variable of sodium (/ 866) and SAR (/ 826) while it had a negative relationship with the variable of soil PH (-258). Then, using multivariate regression, the relationship between the independent variables and the gully length and gully cross section was determined. The results showed that the amount of sand, SAR, TNV and sodium had the greatest role in the longitudinal and cross-sectional area of the gullys , respectively. However, the presence of OC and K prevents the spread of moat erosion in the region. According to the results of the topographic threshold formula of the region (S=6759.756A^(-.556) ), the surface runoff process was recognized as the dominant hydrological process affecting the spread of gully erosion.

gully erosion plays a major role in soil degradation. Rapid progression of moat erosion at the eastern end of the basin In addition to the destruction of a large area of agricultural land and rangeland of the basin, are a serious threat to several villages, especially the villages of Qala-e-Seyed and Chahoo and even part of the city of Mohr. The results of the longitudinal expansion and cross-sectional area of the gullys show the high impact of SAR, TNV and sodium in the soil in creating and spreading the gully erosion. However, OC and K prevent the formation and spread of gully erosion. the soil texture is mainly sandy and loamy and sand has the highest percentage in all samples. Sand particles weaken the soil structure and in spite of water, they lose their cohesion and the underlying layers dissolve and then the upper surface of the gully collapses and causes the development of the gully. The results of topographic threshold showed that surface runoff causes gully erosion so that the lower the slope of the area, the more upstream area and more runoff is produced and easily washed with aggregates with low stability and creating valleys along the route. It causes gully erosion. Based on the results of the research, it is suggested that due to the high amount of sodium, chemical modification should be done first and then the vegetation in the area should be modified and created. Due to the high production of surface runoff, mechanical runoff can be collected through temporary methods including creating temporary dams, rock dams, and soil dams at the end of gullys. It is suggested that combined methods such as CF statistical methods and ME algorithm for modeling be used to more fully investigate the gully erosion of the area.

Classification is one of the most important methods of extracting information from digital satellite images, and today image classification using object-oriented processing using various techniques is widely used. In this research, the object-oriented method in preparing the land use map of Darrehrood catchment area using Landsat 5 images with TM sensor and Landsat 8 with OLI sensor in a period of 30 years, from 1990 to 2019 and its effects on Changes in the discharge of the Darrehrood River were examined. Landsat satellite images are atmospherically corrected with Envi5.3 software and in ecognition software by object-oriented method and nearest neighbor technique using four spectral indices (NDVI, GVMI, EVI, CIG) and average bandwidth characteristics and luminosity characteristics. The image and shape of the objects The images were classified into fourteen classes and the land use of the basin was extracted in the two periods of 1990 and 2019. Object-oriented classification with a kappa coefficient of 93% and based on the overall classification accuracy of 0.9235, The result of the classification by object-oriented method is more accurate and the classification accuracy is at an acceptable level, which among the parameters that were considered to achieve this accuracy can be parameters such as, class neighborhood, band values and Use of spectral indices used to separate units and the number of repetitions of classification operations. According to the changes in the area of the classrooms in the 30-year dimension, it was found that most of the uses are primarily related to the rangeland class, which occupies an area of approximately 1391.42 square kilometers. Then, the use of rainfed and fallow agriculture with an area of 850.55 square kilometers and barren lands with an area of 665.40 square kilometers are the most common areas. These land uses have the most areas in 2019, with the difference that the land use of the rangeland has an area of 1137.06 square kilometers and dryland agriculture has an area of 1013.08 square kilometers, and barren lands have been reduced to an area of/2015 / 35 square kilometers, and instead the area Irrigation has increased from 45.69 to 387.82 square kilometers. Gardens and forests in 1990 occupied an area of 59.79 and 8.78 square kilometers, respectively. In 2019, garden lands will increase to 65.50 square kilometers and forest lands will decrease to 5.49 square kilometers. Increasing the residential land area compared to 1990 has been associated with a decrease in rangelands, which indicates the destruction of rangelands and the creation of residential areas. Eastern was prepared. Rainfall histogram method was used to enter the data into the model. In order to evaluate the land use change in the runoff of Darrehrood catchment in HEC-HMS model, SCS method was used which was implemented in HEC_HMS model and Darrehroud catchment was divided into four sub-basins of Mashiran, Horand, Sambor and Buran and then in the environment. ArcGis software was drawn digitally and the physical properties of the basin and sub-basins were used as parameters required in the present study. Using the SCS method, a soil hydrology group map is required to estimate the CN curve number. Therefore, the map of soil hydrology groups was prepared by the Natural Resources Organization of East Azerbaijan Province to be used to calculate the CN curve number. According to land use in 1990 and 2019, the CN curve number and runoff delay time of sub-basins along with K and X coefficients were introduced to the model and implemented. In Buran sub-basin, there will be a decrease in water permeability and consequently a decrease in initial rainfall losses and an increase in runoff, while in Mashiran, Horand and Sambor sub-basins, a decrease in water permeability and an increase in initial rainfall losses and a decrease in rainfall will decrease. Runoff will be observed. The result of these changes on the runoff of the basin was obtained using the HEC_HMS model. In Darrehrood catchment simulation in HEC_HMS model, calibration of the basin in four sub-basins was calibrated based on runoff peak and runoff height and runoff volume per year. The calculated results with the observed results on average in the runoff height element of 93.15 Percentage and in the runoff peak element 94.35% and in the runoff volume element 94.95% show the correspondence of the correct implementation of the model on the basin, which includes the acceptability of the results (the results showed that the runoff peak in the sub-basin of Mashiran with Reduction of 7 cubic meters and reduction of 8.5 mm of runoff volume in Horand sub-basin with reduction of 8.6 cubic meters of runoff peak and reduction of 12 mm of runoff volume and sub-basin of Sambor with reduction of 2.2 cubic meters of runoff peak and reduction of 12 mm of runoff volume While in the Buran basin, unlike the previous three sub-basins, the increase of runoff peak by 10 cubic meters per second and the increase of runoff volume by 9.6 mm has been estimated.

Morphological Assessment will be necessary to understand the current situation and the potential for possible river changes in the future. Natural factors such as floods, soil erosion, landslides and human factors such as land use change and sand removal from the riverbed affect the morphology. River systems have always been of interest to humans as one of the most vital elements of the Earth's surface. Humans also change the face of the earth by changing their use, destroying natural resources, plowing the land in the direction of the slope, planting trees in the riverbeds. Any Manipulation into the riverbed will change the process of erosion and sedimentation along the river. Understanding the characteristics of flow and sediment is the basis for evaluating the behavior of rivers and deciding on engineering activities. Therefore, it is necessary to obtain the necessary information on how they work before starting engineering projects for rivers.

In this study, geological maps at a scale of 1: 100000 of the Geological Organization, topographic maps at a scale of 1: 50,000 digits of the Geographical Organization of the Armed Forces, Landsat satellite images, 2020 April, November 1995, Climatic data Temperature and precipitation (1399-1374) of Lorestan Meteorological Organization and Digital elevation model of 30 meters has been used. Arc GIS software was used for spatial analysis and ENVI software was used for processing satellite images. The normalized water difference index is the first index of water extraction in images and remote sensing data. In this indicator, two green and infrared bands are used. Positive values of this index indicate water and negative values indicate phenomena other than water. Researchers have proposed different methods for studying changes in river channels. The transect method is used to evaluate changes and displacements in river channels. In this method, lines with specific distances on both sides of the river route are drawn as baselines. These lines are constant for the time periods studied. River channel displacements relative to these lines are quantified. To further evaluate the Kahman River canal, the canal migration rate method was used. The Kahman river Canal was divided into two areas, mountainous areas and plain and agricultural areas, based on topography and land use.

To calculate the area to the right and left of the transects, the Kahman river channel was cut separately with a transect layer in 1995 and 2020. Calculation of changes in the area of transects shows that about 185.85 hectares of land adjacent to the Kahman river (1995-1999) have been eroded. On average, about 7.43 hectares of these lands have been destroyed annually. The maximum value of this index in transect 30 is calculated at 8.27 hectares. In order to better understand the changes and dynamics of the Kahman river Canal, the migration rate index (Rm) was also used. First, two fixed lines were drawn around the Kahman river channel. The area between the two was calculated using Arc GIS software functions. The average migration rate of Kahman river (1399-1374) was 2.51 meters per year. The lowest level of this index occurred in Trasket 49 at 0.18 meters per year. The mountain factor and stabilization operations along the river have been the most important reasons for its control and stabilization. The highest rate of migration occurred in transects 4, 32 and 30 at 4.80, 5.5 and 6.12, respectively. Shortcuts and land use changes have been the main reasons for the high rate of duct migration in these transects. The largest amount of lateral changes in the Kahman river route occurred in parts of the plain and agricultural areas, including transects 30 to 35. The most important factor was the high lateral changes of the Kahman river route in the plain area due to the high erosion of the coastal and floodplain materials. Most of the constituents of the bed and banks of the Kahman river in these periods are from fine to coarse sands.

Duct migration rate index showed that the average displacement of Kahman river canal (1374-1399) was 2.51 meters per year. The lowest value of this index was 0.18 meters per year and the maximum value was 6.12 meters per year. Calculation of changes in the area of transects showed that about 185.85 hectares of land adjacent to the Kahman River (1374-1399) has been destroyed. On average, about 7.43 hectares of these lands are lost every year. In the mountainous area, the effects of the mountains were the most important factor in determining the morphological changes of the Kahman River channel. The presence of erodible materials along the Kahman River in the plains and agricultural areas has increased the lateral migration of meanders and the width of the valley and floodplains adjacent to the river has increased significantly. Therefore, it can be said that the Kahman River has had more geometric changes in the plains and agricultural lands.

Subsidence is the vertical movement or gradual subsidence or sudden subsidence of the earth's surface due to various reasons such as dissolution, melting of ice and compaction of deposits, movements of the earth's crust and the release of lava from the solid crust or activities. Humans such as mining, groundwater abstraction or oil occur (Asadzadeh et al., 2016). The phenomenon of subsidence, which may occur as a gradual or sudden subsidence at the earth's surface, is generally due to major factors such as dissolution of subsurface formations, sediment density and subsidence of groundwater, as well as tectonic factors (Chen et al., 2016). Subsidence can be caused by natural geological phenomena such as earthquakes, dissolution of limestone, sedimentary rocks, ice melting and density of deposits, slow crustal movements and lava outflow from the earth's solid crust, or human activity such as mining. Extraction of groundwater fluids such as groundwater, oil or gas (Gallowey and Burbey., 2011). Radar interference method has been successfully used to measure the displacement caused by earthquakes, glacier displacements, and the phenomenon of subsidence and erosion. Land subsidence as a morphological phenomenon, a type of land deformation that is associated with vertical deformation or downward movement of the earth's surface(Sharifikia., 2011).

The data used in this study include a combination of satellite data from Sentinel 1 radar sensors as well as ancillary data such as piezometric well data for groundwater level estimation and optical satellite images including Landsat satellite images for 2020 was used to extract land use. In the image processing phase, the time series of 2015 and 2020 of Sentinel 1 satellite in C-bar were used. Using image processing with SARSCAPE 5.2 plugin in ENVI 5.3 software platform and using radar interferometry method, the areas subject to subsidence and the amount of subsidence in each area were determined. In the next step, using survey operations, the effects of subsidence in the area were identified. In order to explain the causes of subsidence in the study area, data related to land use as well as information related to observation wells were compared and statistically analyzed with the results of subsidence in the area.

After ensuring that the two images are correct, the resulting phase of the two SLC images is differentiated relative to each other, resulting in an interferometer. Due to the different resolution of this sensor along the range and azimuth and a look in the direction of the range based on the defined mathematical relationship to correct the spectral displacement and Doppler effect was considered. Investigation of subsidence status in the last 6 years, from 2015 to 2020, the amount of subsidence has increased. The red areas have the highest subsidence of 11 cm. The black areas have no subsidence and have a height of 3 cm. When subsidence occurs in the area, it not only decreases, but also has a duality or an ascent. Based on the information obtained from the subsidence map of Shahryar plain, it was found that in the studied period, we see a significant amount of subsidence. In the subsidence map, the highest amount of subsidence was in the eastern part of the city and in the northern part of the city and the lowest amount was in the northwest and northeast of Shahryar plain.

The study of subsidence status in the last 6 years from 2015 to 2020 in the study area shows that the maximum amount of subsidence is 11 cm. In order to reconcile the results of radar interference with other supplementary data, land use as well as piezometric wells in Shahriar plain were used. Co-institutionalization of subsidence plans with land use layers also confirms the maximum occurrence of the subsidence area that the highest rate of subsidence in garden and land uses with a value of 11.54 cm, rangeland with a value of 11.34 cm and residential area uses with a value of They are in the next categories to 10.75 cm. The amount of uplift in all three uses indicates the amount of three cm of subsidence (Table 3). 11 cm subsidence for residential use can provide insecure living conditions for residents and increase their risk of landslides.Due to the groundwater level of the plain, despite seasonal fluctuations, it has a downward trend. As can be seen, in 2015 the water level was 75.85 meters, which has decreased to 68.52 meters in 6 years in 2020. Which shows that during six years we witnessed a 7 meter drop in groundwater.

The purpose of this study was to estimate the amount of sediment of Vanai River in Borujerd. In this research, the characteristics of the sub-basins of this river have been extracted first. These specifications include the physical characteristics of the sub-basins, including the area, the environment and length of the waterways, and the characteristics of the river flow, and its sediment content. In the following, multivariate linear regression, multilevel prefabricated neural network (MLP) and radial function-based neural network (RBF) models are used to model sediment estimation. After estimating the model, the mean square error index (RMSE) was used to compare the models and select the best model. Evidence has shown that initially the MLPchr('39')s neural network model had the best estimate with the lowest error rate (90.44) and then the RBF model (151.44) among the three models. The linear regression model has the highest error rate because only linear relationships between variables are considered.

Land use reflects the interactive characteristics of humans and the environment and describes how human exploitation works for one or more targets on the ground. Land use is usually defined based on human use of the land, with an emphasis on the functional role of land in economic activities. Land use, which is associated with human activity, is changing over time. Land use information and land cover are important for activities such as mapping and land management. Over time, land cover patterns and, consequently, land-use change, and the human factor can play a major role in this process. Today, satellite-based measurements with geographic information systems are increasingly being used to identify and analyze land-use change and land cover. Therefore, accurate detection of changes in land surface properties, especially LULC changes have become a key issue for monitoring local, regional, and global resources and environments, Providing a basis for a better understanding of the interactions between humans and natural phenomena and the proper management and use of these terrestrial resources. About the problems of changes and transformations in the studied area, remote sensing can allow managers to categorize images and evaluate land-use changes, in addition to saving time and costs, which allows planners to make plans based on changes, more resources are lost, to be prevented.

The Gamasiab River originates from calcareous springs located 21 kilometers southeast of Nahavand in Hamadan province from the northern slopes of the Greene Highlands known as the Mirab Gamasiab. This river enters Kangavar, Harsin, and Bistoon Kermanshah from the east-west direction of Nahavand and then enters the Faraman area by going around Bistoon and continues its north-south direction after receiving other branches and water. The surface currents of the adjacent basins join the Gharasu. For this study, an approximately 80 kilometers interval from the Gamasiab River and its adjacent lands 5 kilometers from each side was selected. Three images of Landsat for TM, ETM+ and OLI sensors were selected for monitoring of the river adjacent lands and vegetation indices for the years 1987, 2000, and 2017, respectively. NDVI is the normalized difference vegetation index and is the most common vegetation index. SAVI Soil- Adjusted Vegetation Index by Huete (1988) has been developed to use soil optical properties on the canopy reflectance capability. This index has added a factor of L (soil texture correction factor) to the NDVI equation. Radiometric and atmospheric correction images are performed before applying spectral indices. In the process of atmospheric correction, the first step is to calculate the radius value, and from the radius value, the reflectance value is calculated. There are two advantages to using reflectance values compared to radiative values: first, the effect of the cosine angle of the different solar angles can be measured relative to the time difference between the data harvesting, and second, the different amounts of solar radiation outside the atmosphere caused by the differences. The band is spectral, corrected. Atmospheric correction is done to eliminate the effects of the transmission and absorption of electromagnetic waves in the visible and infrared range. In general, each of the terrestrial features has a special spectral sign (spectral signature). These spectral signatures depend on many factors, such as sensing properties, differences in radiation and reception angles, atmospheric and topographic conditions, and imaging time. Because of the factors mentioned above, digital numbers (DN) cannot represent the actual conditions of spectral reflection of the Earth. The purpose of radiometric correction is to remove or neutralize the above effects of the image. After the indexes are applied, the land units have to be separated so that they are threshold on them, which means that we separate the pure classes. So, values between -1 to 0 are considered as wet and water body, values between 0 to 0.3 as soil, and values between 0.3 to 1 as vegetation.

Due to the lack of user interference against the object-oriented and pixel-based classification algorithms, this process (applying spectral indices) uses spectral information of the bands used, as accurate as the radiometric resolution of the sensors used. The results showed that for the NDVI index in 1987 the amount of water land fields was 13% and this value decreased by 0.63% to 12.57% in the year 2000 and 18.71% in the whole study area in 2017. These figures for the SAVI index in the year 1987 amounted to 10.42%, for the year 2000 the value was 10.93%, and for the year 2017 amount was 16.54% of the total area studied. What is certain is the slight change in 2000 relative to 1987 for both indices. Both NDVI and SAVI show an increasing trend of vegetation cover (water land fields) in 2017. These figures show the unprecedented use of river water and groundwater in recent years. Excessive use of groundwater resources results in land-use changes and subsequently physical, chemical, and even biological changes in water resources and land surface. For soil class, it is clear that both NDVI and SAVI indices show slight changes from 1987 to 2000, and for 2017 both indices show decreases in soil class compared to 1987 and 2000. The results show the inefficiency of indices in river and water body extraction in the study area. Principal components analysis was used for river extraction. Consequently, by comparing the indices with the corresponding PCAs it can be said that the river is properly extracted using PCA which can lead to even better results for the wider rivers.

Identifying and discovering the land cover changes can help planners and planners identify effective factors in land-use change and land cover, and have useful planning to control them. High accuracy maps are required for this purpose. The use of spectral indices makes this possible with very high accuracy. The results of this study, in addition to prove the accuracy and efficiency of spectral indices for estimating land cover, showed that during the years of 1987, 2000 to 2017 the soil class reduce and, on the other hand, increased water land fields a general trend This illustrates the general trend of degradation in the region through the replacement dry-land fields than water land fields.

Floods are one of the most destructive and most frequent natural disasters. In this regard, flood risk zoning is one of the most effective methods for managing and mitigating the effects of floods. In this study, the spatial and risk assessment of flood events at the surface of Ghorichai watershed in Ardabil province was investigated. In this regard, 10 measures affecting the flood event were applied. These criteria include elevation, slope, slope orientation, convexity, geological formations, drainage density, curve number (CN), waterway distance, land use and vegetation. In the meantime, the ground slope variable with a weight of 0.26 (excluding network analysis process model) plays a major role in identifying high risk flood zones. Two fuzzy logic models and a network analysis process (GIS) were used to integrate and overlap the aforementioned subject layers in order to prepare a flood risk zoning map. Flood hazard zoning of the Ghorichai watershed showed that about 18% of the studied watershed area was located in high and very high risk classes. Flood risk in the basins of the main valleys and downstream lands of the study basin due to Geomorphometric reasons such as formation and development of flood plains, low relative elevation, concave terrain, and rapid upstream runoff of the event potential. It is high. In addition, relocation of residential areas downstream of the basin has increased the risk of flooding in these areas. Flooding is called the dangerous increase in the flow of a river or a flood. This phenomenon has a long history in human history and is one of the most damaging and destructive natural events. Old towns are usually formed alongside rivers due to easier access to water; they are therefore affected by floods, causing casualties. Floods occur when water flows out of rivers, streams and canals, in other words leaving its natural channel. We see an event when the canal or river is completely filled with water and enters the floodplains and areas where people live. In the present study, the risk of flooding in the Ghorichai watershed is investigated. Due to the large extent of the semi-arid climate and the presence of numerous settlements in the region, it is important to assess the importance of flood risk assessment and zoning in the Ghorichai catchment area. This is especially important in the presence of large human settlements, extensive agricultural lands and conservation of water and soil resources. Therefore, flood risk zoning at the basin level of the study is one of the essential steps in the management of flood risk mitigation and management measures.

The present study investigates the risk of flooding in the Ghorichai watershed. This basin is located in Ardebil province within the administrative districts of Nair, Ardebil and Kosar. Much of this basin is located in the Nair area. The study area is located at 48 degrees 2 minutes to 48 degrees 31 minutes east longitude and 37 degrees 46 minutes 38 degrees 11 minutes north latitude in mathematical position. The basin has an area of about 824 km2 and an area of about 240 km. In this study, the following data and tools were used to analyzed flood risk in the Ghorichai catchment: 1: 50000 area topographic maps, 1 scale geological maps : 100000 including Ardebil and Givi Sheets, 1: 250000 Scale Soil Map, Digital Elevation Model (DEM) for ALOS - PALSAR Satellite Area with 12.5 m Resolution, Sentinel2 Satellite Images with 10 m spatial resolution and meteorological and climatic data including Ardabil synoptic station data and rain gauge station data located within the watershed. Two models of fuzzy logic and network analysis process (ANP) in the framework of Geographic Information System (GIS) were used to model flood risk in the Ghorichai catchment area.

The final layer resulting from the composition and overlap of the subject layer indicates the potential for flood risk at the Ghorichai watershed. The resulting map was classified into five classes of very low, low, medium, high and very high flood risk. According to the flood risk zoning map of the Ghorichai catchment, it can be stated that approximately 49 km2 of the catchment area is in a very high flood risk class, covering about 5.9% of the catchment area. In addition, more than 101 km2 or about 12% of the study area is in high risk class. Most of these high-risk areas are adjacent to either side of the basin's main waterways or floodplains adjacent to them. This can be attributed to a number of reasons, such as the smoothness or slowness of these areas (and thus the possibility of easier spreading and spreading floods), the existence of a valley extended by the flood plain below it. , Counts the crossing of the Ghorichai River through these zones and the low altitude of these zones.

In this study, in order to map the flood hazard in the Ghorichai catchment area, 10 factors influencing flood event were applied. These criteria can be divided into three main categories: geomorphological criteria including altitude, slope, slope direction, ground convexity and geological formations; hydrological criteria including drainage density variables, curve number (CN) and distance from the waterway. And land cover criteria including land use and vegetation (NDVI). To integrate and overlap the research thematic layers with the aim of mapping flood risk zoning at the Ghorichai watershed, we use two models of fuzzy logic and ANP in the form of GIS. There was action. In this regard, all subject layers affecting flood occurrence were applied with different fuzzy functions, in the range between 0 and 1 standard and having the same units. Then all relevant subject layers were combined with the weights obtained from the ANP model. Flood hazard zoning of the Quriichai watershed showed that about 18% of the studied watershed area was located in high and very high risk classes.

In recent years, with the growing population, industrial development of groundwater resources has more than doubled, with groundwater levels continuing to fall and eventually reaching a point where there will be no more water to extract. Therefore identifying these resources, making optimum use of them means a permanent and permanent harvest of this natural gods wealth. 

In this first phase, field studies and data collection were done. Wells map was prepared first and then effective parameters were identified: elevation layer, slope, slope direction, surface curvature, topographic moisture index, land use, soil, geology, river distance, drainage density, fault distance, fault density specified. And their plan was made. Satellite images of ENVI and eCognation software were used for mapping the land and the images were classified with basic pixel and object crosshairs and fuzzy logic and artificial neural network methods were used. Based on fuzzy logic, baseline maps are first ranked based on their impact and importance on groundwater resource potential, and then determined using a fuzzy method for each specific class rating factor. In the neural network method, these agents, along with a number of wells, enter the network as the input layer. In this way, the pattern is trained by the network between the input parameters (network input) and the areas where potential water resources exist (network output), then for The input parameters of the catchment to the trained neural network are predicted corresponding outputs which are potential areas of groundwater potential. Then the results of both models are tested and finally by comparing the results of the neural network model with the fuzzy logic model an appropriate method for groundwater resource potential in the catchment is obtained and the most important factors in resource potential in the area are identified.

Evaluation of effective layers in the potential of groundwater resources: The results of the elevation factor showed that the highest percentage (33%) of the area with high potential is located at an altitude of 1700-2000 m, which is the average altitude of the region, and the results of the slope factor study show that the highest percentage of high and medium potential areas is on the slope 0-1 is located and the highest percentage of areas with low potential is on the slope of 60- 173 and the highest percentage of areas with high and medium potential to the northeast direction and the highest percentage of areas with low potential is in the southern direction. The results of the study of lithology class (formation) show that the highest percentage of areas with high and medium potential. The weak are in the class (reserves of old and new mountaineering terraces and conifers). And the highest percentage of soil in the region is in areas with high potential and weak in the group (rocky / intulse outlets) and the highest percentage of areas with medium potential in the group (rocky direction outlets / input solo).The results of the study of the distance from the river show that the highest percentage of areas with high potential is located in the nearest distance from the river 200 meters and the highest percentage of areas with medium and weak potential is in a range far from the river. The results of the fault gap survey show that the highest percentage of areas with high and medium potential is located in the closest distance from the fault (0-0.032) and the highest percentage of areas with low potential (0.115-0.170). Precipitation and temperature estimates show that the highest percentage of areas with high, medium and weak potential is in the range of 756-844 and the highest percentage of areas with high, medium and weak potential is in the range of 13-15. And the highest percentage of areas with high and low potential is in the use of medium rangeland and the highest percentage of areas with medium potential is in forest use. The results of the groundwater map show that the highest percentage (36.85) of areas with high potential is in the class of 10.12-6.74. Evaluation of the classification maps of the results shows that the accuracy of kappa in object-class classification is 96% and in base pixel 85%. Neural network results also show that about 39% of the area has high potential of groundwater, while fuzzy logic map shows about 87% of the area with low potential.

Using the descriptions needed for basic pixels and taps, we can provide you with faster access to various sites, loops, content and technical and engineering information in a variety of areas, in the entertainment and leisure markets. Read yourself below. From this research it can be concluded that your neural network can control the energy potential of water Underground is more practical than the appropriate fuzzy logic method because its results are closer to the ground. Factors such as slope factor can be considered as an important factor in the potential of groundwater resources because of the high percentage of potential areas in the range of 0-10%.

 Landslide is a term that encompasses a variety of amplitude motions and causes the movement of a mass of material in the slopes. And creep is classified. Natural slope instability is one of the geomorphological and geological phenomena that plays an effective role in deforming the earth's surface. Identifying areas with potential for landslides and their zoning is one of the key steps in managing environmental hazards and reducing the damage caused by this phenomenon, because this phenomenon causes financial and human costs, soil and land degradation and increased sediment production at the basin outlet. It becomes. Iran with its predominantly mountainous topography, high tectonic activity and seismicity, diverse geological and climatic conditions, has the most natural conditions to create a wide range of landslides. The purpose of this study is to zoning the risk of landslides on the Sarab-Nir road. In this research, two models of network analysis and fuzzy logic are examined and evaluated. It is hoped that eventually, by preparing a landslide risk zoning map, it will be of great help to planners and managers in order to reduce potential damages and find safer locations for development, construction and road construction.

Sarab-Nir road is located between East Azarbaijan province and Ardabil province and is a communication route between these two provinces, whose geographical coordinates are 37 degrees and 94 minutes to 38 degrees and 03 minutes north latitude and 47 degrees and 53 minutes to 48 degrees and 01 minutes. It is east longitude. In this study, network analysis model was used to determine areas prone to fall and zoning. In order to better understand the causes of landslides and also to organize the research in the field, the study area was visited and 15 geographical points from different areas of the study area were recorded. The geographical location of the points prone to fall was also recorded with GPS. Then, according to the network analysis model, information layers were prepared in ArcGIS software. The information layers for landslide risk zoning are: fault, slope, slope direction, distance from road, and distance from waterway, land use, geology (lithology), precipitation, altitude and vegetation. The elevation file or digital model of the elevation of the area was prepared with an accuracy of 30 meters from the USGS site and the desired DEM is a digital file obtained from the AST‌ER sensor and according to this DEM, the information layer such as streams, slope and direction ‌The slope was obtained.

Four maps have been developed to investigate landslide hazards, which are rainfall, slope, elevation and land use layers. After creating information layers in order to prepare the final landslide hazard map, fuzzy information layer maps were created. In this study, in order to determine the effect of different classes of criteria on landslide sensitivity zoning, the layers are based on the type of performance of each in the landslide event using fuzzy membership functions in the range of zero to 1 fuzzy. Were made. The results obtained from the information layers and finally the landslide hazard map show that altitudes of more than 2000 meters have the highest share of landslides, and altitudes of 1400 meters have been significant landslides due to the instability of the slopes against Climatic and environmental factors. Also, most of the landslides occurred at a distance of 3 to 6 km from the faults, which shows the importance of faults against landslides. About 40% of landslides occur in very high-risk classes. This indicates that the model has a high capability in predicting landslides. It is necessary to explain that most of the landslides occur in the area of Saein pass, which have very favourable conditions for the occurrence of range movements that start from 25 km of mirage and continue for a distance of 15 km of Nir.

Factors such as slope, precipitation and geology play a more important role in landslides than other factors. Slopes of 60 to 80% have the greatest impact on landslides, which are more pronounced at altitudes above 2000 meters. Therefore, altitudes above 2000 meters have the most landslides. Also, due to the direct relationship between altitude and climatic fluctuations in these altitudes, the amount of precipitation is higher and, of course, has a great impact on the occurrence of landslides. In these areas, vegetation is at a minimum and due to the cold region, the vegetation in these areas is very small, which prepares the conditions for landslides and due to the presence of sedimentary formations such as sandstone, Siltstone mudstone with tuff interbreeds in the area, the conditions for landslides have become more prone and because these formations lose their stability sooner and are strongly influenced by physicochemical factors, they are more prone to landslides than other formations. To be. According to the results, the low risk floor with the highest value, 405.44 square kilometers, occupies approximately 30.87 percent of the area, but the very high risk floor with 288.2 square kilometers and the high risk class with 23.23 square kilometers. , Occupy a total of 37.25% of the area of risk classes.

Water is one of most important human needs for life. According to importance of subject, discussion of management and utilization of water resources has become one of the most important global issues. Remote sensing data are often used in water body extraction studies and type of remote sensing data used plays an important role in water body extraction. In this study, ability of Landsat satellite with application of water indices, to extraction of Gamasiab River in Kermanshah and comparing these indices have been investigated. Specific feature of Low width and shallow rivers has increased the complexity of studies of such rivers using available data. Water body extraction from remote sensing images has been over the past two decades. Water indices were first developed using Landsat TM and Landsat ETM. But its better performance in Landsat 8 is well documented by the researchers. In this study, NDWI, MNDWI, AWEI_nsh, AWEI_sh and WRI indices were used. With extracting optimal threshold from histogram of indices and applying this threshold, the study area was classified into two classes of water and non-water. Then overall accuracy and kappa coefficient values were taken from each of the indices. Finally, AWEI index with overall accuracy of 99.09% and a Kappa coefficient of 0.98 was the best response among the indices in the study area. The results this study showed that approach can easily extract water from satellite imagery.

Instability of natural slopes is one of the geological and morphological phenomena that has a significant role in changing the form of surface of the earth, and when it affects human activities, it can become a dangerous phenomenon (Esfandiari, 2006: 113). Landslides as geological events related to the transportation of soil / heavy rock materials and assessment of its sensitivity, is an important task for local authorities to plan and reduce the land (Xialong Deng, 2017: 2). Therefore, many attempts have been made to assess the dangers of mass movements, and it is suggested to have its reduction methods based on the key characteristics of the slip, including scope and extent, volume, startup mechanism and recurrence, and subsequently, make decisions (Kuo Jeong Chank et al., 2018: 700). (Hemati and Hejazi 2017: 24-7) evaluated the landslide hazard zonation of Lavasanat watershed using logistic regression statistical methods and the result was stated in this way that in the studied area, areas with high risk of zoning, had a large share of the area amount of ​​the region.  Aliabad basin with the southwest - northeast trend in the geographical coordinates of -   located in the east and - located in the north latitudes of the northeast of East Azarbaijan Province and southeastern part of Horand County.(Figure1) Figure (1): Geographic location of Aliabad watershed

 Topographic map (1: 50000) and geological map of Kaleybar region (1: 100000). 2- Landsat satellite images of 8 OLI sensors 3- GPS devices 4- Maps of the faults, slopes, isohyet, isotherm, evaporation, land use, elevation and hydrology 5- Envi 5.3 software 6- Statistical software of SPSS, version 16. For zoning the risk of rock falls, nine layers of information including slope, hypsometry of the region, isohyet, isotherm, evaporation, distance from the fault, distance from the river, land use and lithology were used as independent variables and to prepare the layers in Arc GIS, 1,500,000 topographies and 1.100000 geology maps were utilized, and Landsat 8 satellite imageries were used with the OLI sensor to produce the land use layer. So, after preparing the considered data, the layers were classified as raster, and in their descriptive table, a column called the standard weight was added and the classes related to each layer were calculated using a sum ranking method. In this research, the rock fall layer was considered as the dependent variable and the 9 presented layers were considered as independent variables and all layers had been evaluated in the normalization of the weight between zero and one per pixel; based on the proportion table method, each layer, having 500 weighted pixels that overall included 5000 pixels, was entered into the SPSS environment and regression analysis was performed thereof. Independent variables, including 9 variables, consisting of three PhDs in geomorphology and two Phd in geology were selected based on exports opinions considering their importance in creating and strengthening the dependent variable were weighted between zero and one numbers.

The Chi square test for each of the independent variables, separately, showed that there was a significant relationship between the independent variables and the dependent variable, and the effects of these variables on the dependent variable was acceptable. The numerical value of R was 0.953, and if the R value was closer to one, it would indicate the high validity of the test. The numerical value of the coefficient of determination of the independent variables relative to the dependent variable was 0.909, which indicated the high validity of the significance of the test, because it was closer to number one. Of course, it is clear that the value of the determination coefficient in Pseudo R Square was determined to be good, so the adjusted coefficient of determination was considered whose numerical value was 0.907. These findings indicated that roughly 90 percent of rock falls occurred in the Aliabad basin have been affected by these 9 estimated independent variables. Given that the statistical analyzes confirmed the validity of the effects of independent variables on the dependent variable according to the weightings of the experts in terms of zero and one for each variable as well as the importance of the variables in relation to each other as a binary comparison, the zoning of the risk of rock fall for the Aliabad watershed of the Horand basin was done using Arc Gis software, and in this zonation, five falling risk classes were used including very high, high, medium, low and very low .

lithology and the distance from the fault and river and foot slopes were the most important factors in the formation of rock falls since the drainage system of the basin exactly followed the fault zone. The reason for this issue can be analyzed in the way that the longitudinal distance of the highest parts of this region, from the basin to the Aliabad River was lower, which has caused the slope of the basin to perform deep slices to achieve a balance in the slopes and hydrology. The southern parts of the basin are considered as one of the most susceptible basins in the geomorphologic phenomenon of rock falls and destructive cones due to the existence of alluvial formations and the lack of proper slopes and the relative reduction of the fault to the northern and eastern parts despite having significant heights and very low and low status of zonation in the risk of rock falls, and in the southwestern part of the basin, a presence of rocky outcrops in the presence of permeable cones has been also observed. This issue should be addressed to the authorities in order to avoid serious damages to the lives of the inhabitants of the basin, so that the potential risks of this phenomenon could be controlled as much as possible including: threatening communication routes and threatening rural villages and damaging electrical and telecommunication facilities, therefore, infrastructure solutions should be applied in this regard.