فصلنامه پژوهش های ژئومورفولوژی کمی
سال دهم شماره 3 (پیاپی 39، زمستان 1400)

One of the areas that has high potential in the development of geotourism and tourism industry is the karst areas. Iran covers about 11% of the country's karst areas, most of which are located in the Zagros belt.. Kamyaran city in Kurdistan province is one of the areas located in karst formations and due to the geographical location and the existence of appropriate parameters such as rainfall, lime purity, the presence of seams and cracks, etc., has caused most of the karst areas of this area to be developed and this factor has caused in this city We see a variety of karst landscapes such as caves, valleys, rivers and abundant springs and waterfalls, and also on a smaller scale types of lapis that play a role in the diversity of landscapes in the region. The diversity of karst landscapes in the region has caused this city to be very popular with tourists in recent years, so the karstic nature of the region has been the main factor in attracting tourists in recent years, but despite the karst factors as Has been the main factor in the development of tourism in the region, However, no comprehensive research has been done on the geomorphological resources of tourism and their vulnerability, so in this study, the effective factors in the development of karst landforms and also the impact of the role of karst landforms in the development of geotourism in Kamyaran city have been studied.

This research is based on descriptive and analytical methods. Research data include geological maps 1: 100000, topographic maps 1: 250,000, digital model of 30 m height, as well as library information and information from field visits. In this research, ArcGIS and SPSS have also been used as research tools. This research has been done in two stages. In the first stage, potential library geosites have been identified using library studies and field visits. After identifying the geosites, they were evaluated using the Kubalikova method and the local method, which was prepared based on different methods. In the second stage, in order to identify the karst areas developed, 9 parameters including slope, slope direction, altitude, distance from the river, precipitation, temperature, distance from the river, distance from the fault, lithology and land use have been used. After preparing the information layers, in order to do the final zoning, the prepared layers are fuzzy. After preparing the information layers and fuzzy them, the information layers have been weighed using the opinions of relevant experts and the network analysis model (ANP). After weighting the information layers, the obtained weight is applied on the layers and finally the information layers are combined using a fuzzy gamma operator, thus preparing the final map of the developed karst areas.

The results of the evaluation of geosites indicate that according to the Kubalikova method, among the geosites of the region, Palangan valley has the highest score with a total of 10.25 points, followed by the geosites of Gavoshan dam and Tangivar river. With 10 and 8.75 points, respectively, have the highest score. According to the local method, Palangan Valley geosite has the highest score with a total of 28 points, followed by Tangivar River and Amirabad Plain with 26 and 25 points, respectively. Also, based on the results of combining the two methods of Kubalikova and native, the Palangan valley geosite with an average of 74.4%, has the highest score, so it is the most valuable geosite in Kamyaran. After this geosite, the geosites of Tangivar River, Gavoshan dam, Morvarid neck and Vian valley have the highest score with an average of 66.2, 66, 62.7 and 61.4 points, respectively. The results of identifying areas prone to karst development also indicate that the western parts of Kamyaran city, corresponding to the Shaho slopes, have a high potential for the development of karst processes.

The results of the research indicate that a large part of Kamyaran city is covered by developed karst areas, which has caused this city to have a high geotourism potential. In fact, under the influence of hydro-climatic conditions, geology and geomorphology, many parts of the city, including its western and southwestern regions, are prone to the development of karstic processes. Influenced by karstic processes, Kamyaran city has various forms of geotourism including springs, karstic valleys, rivers and also various landscapes resulting from dissolution that the distribution of these geosites is directly related to the development of karstic areas. In fact, a large part of the geosites of Kamyaran city are located in developed karst areas. Developed karst areas, along with their high geotourism potential, are exposed to vulnerabilities due to pollution and degradation, which should be considered in the geotourism development goals of the region.

Since there are not enough tools to measure flood, erosion and sediment in many watersheds of the country, it is necessary to use indirect methods such as fractal geometry to estimate them. There is very little accurate information about erosion in our country (Mohammadi et al. 2008). Understanding the sedimentation status and sedimentation of basins provides an accurate understanding of erosion and its consequences (Piri et al. 2005). Some parameters of watersheds have a special geometric shape that can be examined with fractal geometry. Mathematically, the basins that have the same fractal dimensions are equivalent to each other and are very similar in terms of geomorphological and hydrological characteristics (Adl and Mehrvand, 2004). The aim of this study is to obtain a significant relationship between fractal drainage network and erosion and sedimentation rates, and to generalize the results to unmeasured areas. 2. Introducing the studied area The studied area consists of 12 basins of Ilam province, which are in the western foothills of Zagros Mountain. Figure 1- The position of studied basins in the country and in the Ilam province Table 1- Specifications of basins and their stream gauging stations

These networks were provided based on 50DEM coordinates that in many cases, there isn’t enough accuracy and some channels are not displayed. Therefore, after transferring data to Google Earth, it was fully matched with the natural drainages and with a 5-meter accuracy, hydrographic network map was drawn and completed to reflect the full details of the network.Thence one cannot scale maps via “Fractalys”, fields with the same space of 25 kilometers on similar formations in different areas were accidentally chosen via “Fish Net” –in Arc GIS, to fix this problem. For each study formation, three 25sq.km. Fields were chosen and by the accuracy of 5 meters. These maps that had the same drawing accuracy and space, were drawn in the same scales via GIS on an A4 page in .bmp” and then were brought to Fractalys and finally, their fractal dimensions were calculated and extracted by the geometric method of counting boxes.

Figure 2- Hydrographic network and fractal dimension of the nazarabad watersheds before hydrographic network modification Table 2- Fractal dimension of watersheds before and after hydrographic networks modification In the following figures, the calculated fractal dimensions are observed for several samples of 25 km units before and after the hydrographic network modification. After the modification before the modification Amiran Aghajari 1.134 1.481 1.149 1.435 Figure 3- Fractal dimension of a hydrographic network of Aghajari and Amiran formations before and after hydrographic network modification. Quaternery Gachsaran Figure 4- Hydrographic network modification in the 25km unit on Google Earth Figures (3) to (4) show that after hydrographic network modification, the density of the hydrographic network and consequently the fractal dimension are increased in units of 25 km. Also, hydrographic network density changes in more sensitive formations are more than resistant formations, so their fractal dimension changes are also higher.Figure 5- Investigating the correlation of fractal dimension with hydrological indexes of Ilam watersheds the R2 value that is representing the correlation value is 0.0905. Therefore there is no significant relationship between the specific flood discharge of watershed and its fractal number. Table (3) Correlation test of specific flooddischarge data (Qw) in terms of (m3/s/ Km2) and fractal number (Fr) of the basins after modification of 25km units In Table 3, the specified number (-.240) indicates the correlation value of the data. Due to the obtained value, there is no correlation between the specific flood discharge and the fractal number of the basin.Figure 6- Correlation line chart of specific flood discharge data and fractal number of basins after modification of 25km un its Figure 7- Investigating the correlation of fractal dimension with the sedimentation index of Ilam watersheds In Figure 8, Due to the R2 value (0.939), it can be also concluded that there is a significant and direct correlation between the specific sediment discharge value and fractal dimension of the watershed. The following tables show the results of the calculations performed in SPSS software. Table 4- Correlation test of specific sediment discharge data (Qs) and fractal number (Fr) of basins after modification of 25km units The results of SPSS in Table (4) show that there is a high correlation between the specific sediment discharge data and fractal number. Because the number of 0.996 equals the correlation value between the two variables of the specific sediment discharge and the fractal dimension of the basins. The dispersion of data in the figure represents a high correlation in the data. Because the data are not scattered.

The fractal dimension gives more accurate results by the box-counting method than the magnification and radial methods..The results of the research show that there is a significant and inverse relationship between the fractal dimension of the formations and their resistance to erosion.As the strength of the formation increases, its fractal dimension decreases and therefore the density of the hydrographic network is lower.There is no regular trend between the hydrographic network density and the fractal dimension of the basin with the specific flood discharge of basins.Also, there is no significant relationship between this index and the fractal dimension of the basin.There is a significant and direct relationship between the fractal number and the specific sediment discharge (at a level of 5%), which indicates the erosion and roughness rate in the basin The highest value of fractal dimension can be observed in areas that are very sensitive, including Doiraj basin to 1.49.The least value of fractal dimension can be observed in areas that are resistant to semi-resistant in terms of geological formations, such as Kolm and Chamagaz equal to 1.14 and 1.11, respectively.

A flood occurs when the river flow can no longer be contained within its bed, and over spills its banks. Flooding is a natural and regular reality for many rivers, caused by any pulse of overflowing water that overwhelms a river channel, which supports the most naturally dynamic ecosystems. However, humans often perceive floods negatively due to damage and loss of life. Flooding is the most widespread hydrological hazard worldwide that affects water management, nature protection, economic activities, hydromorphological alterations on ecosystem services, and human health. The mitigation of the risks associated with flooding requires a certain management of flood zones, sustained by data and information about the events with the help of flood maps with sufficient temporal and spatial resolution. This paper presents the potential use of the Sentinel-1 images for flood mapping in monitoring the flood that occurred during March 2019 in the Golestan province. More specifically, in this study, we describe accurate and robust processing that allows real-time flood extension maps to be obtained, which is essential for risk mitigation.The aim of this paper was the detection of area susceptible to flood in the Golestan province using Sentinel-1 SAR images and modeling response of area susceptible to changes of Gorganroud river discharge using the HEC-RAS hydrodynamic model during different return periods. The peak discharge data recorded at Golestan province hydrometer station was employed to predict peak discharge values during different return periods.

The Gorgan Plain, northern Iran, is part of the larger Aralo-Caspian depression which experienced strong subsidence during the Middle Pleistocene, resulting in the gentle inclination of the plain to the west (average slope of ~0.6%). In this study, it is been used Sentinel-1 SAR images because of their features, configuration, and the free data set available online from Earth Observation data resources. Sentinel-1 SAR datasets, coming before and after the event, were downloaded via the Copernicus Open Access Hub platform. Also, for the perpose of modeling, the peak discharge data recorded at Golestan province hydrometer station was employed to predict peak discharge values during different return periods.

SAR data is preferred for flood mapping and real-time monitoring in all weather conditions. In this study, dual-polarized (VV and VH) Sentinel-1 SAR images coupled with hydrological data (peak discharge data) were used to produce flood inundation maps. Thresholding technique has been applied to determine the flood mapping through Sentinel-1 data. VH and VV polarisation methods have been applied for a comparison of their respective accuracies in delineating surface water. The finding reveals that the most accumulation of flood took place in the channels with a massive semiment accumulation surrounding the agricultural land and residential region. The proposed approach demonstrates that the microwave remote sensing data along with GIS can be used efficiently for flood inundation mapping, monitoring, and analysing its effect on channel morphology. Therefore, the results of this study will help to take the initiative to reduce the flood hazard impact in the doab area and increase the flexibility in the process of flood management.

The present study exhibited area susceptible to flood in the Golestan province using Sentinel-1 SAR images and modeling response of area susceptible to changes of Gorganroud river discharge using the HEC-RAS hydrodynamic model during different return periods. Flood occurs due to rapid population growth, land degradation, and climate change, and causes harmful damages to human beings and properties. This can be minimized by giving attention to flood risk measures. This study was aimed to map flood inundation areas along the Gorganroud River using Sentinel SAR images, GIS, and HEC_RAS. Flood inundation mapping is used to define the zones which are more susceptible to flood along the Gorganroud river. Using the past peak discharge data and the release of floods related to 2019, Golestan, besides topographic data, maps were made to illustrate areas predictable to be covered with the flood for different releases. The flooded areas on Gorganroud have been modeled using peak flows for different reoccurrence eras using the HEC-RAS model, GIS for spatial data handling, and HEC-GeoRAS for interfacing among HEC-RAS and GIS. These critical floods were damaging the areas around the River, which is hazardous to social and economic growth due to loss of lives and destruction of properties. Residential areas and agricultural lands are located along the river banks and are highly susceptible to flooding for all return periods. Generally, this study discovered that flooded areas in the upstream and middle parts of the River are high as related to the downstream parts.

At the present time, remote sensing can provide the opportunity for mapping lithology, mineralogy, altered rocks, and environmental pollution, and is a useful tool for acquiring basic information, particularly on a regional scale. Significant phenomena in the field of geology are evapotranspiration, including salt domes. Evaporative structures are geological formations that are geographically expansive. One of the important morphological phenomena associated with this evapotranspiration is the structural development of salt domes. Salt domes The structures of geology are a dome of the shape formed by the movement of salt and its ascent in the diaphysmic mechanism. Salt domes and adjacent sediments are examples of a complex geological environment. Their study is due to the unique tectonic and lithological properties of salt, The existence of important resources in terms of the economic aspect and the effect of these evapotranspiration zones on the quality of resources around the salt domes is of great importance in geology, management, and human resource planning. Remote sensing technology in recent years has taken a strong role in obtaining information from these unique phenomena. So satellite imagery classification is one of the most important stages in the interpretation of satellite data, which allows users to produce various types of information, such as the production of covert maps, usage and discoveries of changes and influences.

This section consists of three steps: (1) In this study, the Landsat-8 satellite imaging imaging (OLI) image sensor on November 15, 2014 was used to carry out remote sensing studies for the classification and mapping of the global salt dome.(2) The data preprocessing stage is one of the most important steps in image processing, since all subsequent calculations are based on the image produced at this stage. The type and type of operation of this operation will vary depending on various factors such as the type of data used and the purpose of the research. In the process of preprocessing satellite imagery, it is necessary to remove any errors, such as atmospheric effects, before the identification and extraction of information.(3) PCA: The principal components analysis method is aimed at compressing the dataset in different bands of an image and in order to remove similar information. The main components of decomposition in the interpretation of digital remote sensing data are of great importance. The most important benefits of the main components of collecting and aggregating information on phenomena in different bands are less in a number of bands or components, in other words, the main components To remove excess data in satellite data, it is used extensively. The output of this method is usually a new and limited range of bands whose correlation between them is minimized, so they can be interpreted non-dependent on the original data. In general there are three stages in the classification of the neural network. The first step is an educational process using input data and educational prototypes. The second step is the validation phase that determines the success of the training and network authentication, validating and testing the network by some non-teaching samples. The last grade is the classification stage, in which a map is classified based on educational relationships during the alignment phase.

When the results of the tables are examined, several conclusions are drawn:It was observed that the class of sand-salt with 100% accuracy, clay class, 96.05%, gypsum-salt class, 99.33%, limestone class 100%, class of plants, 96/73%, sandstone class, 94/67% Salt rocks are 96.9%, gypsum soils are 93/58%. It is noteworthy that the lowest accuracy between classes is shale, which is 86.73%. Of the 185 pixels of this class, 170 pixels are correctly positioned on the shale floor, 1 pixel on the floor of the gypsum salt, 1 pixel on the clay, 3 pixels on the floor of the plants, 2 pixels on the sandstone floor, 1 pixel on the rock floor Salt and 7 pixels in gypsum soils are in error in other classes. ), It can be said that the artificial network method with the correctness of the total of 95/3501% and Kappa coefficient of 94.37% have a good performance in classifying and preparing the map of the study area.

With the launch of Landsat in 1972, remote sensing technology has opened a new horizons in the planning, research, assessment and management of natural resources. This phenomenon provides a new method for efficient and effective mapping of various terrain zones, including salt domes. Detailed information can be extracted from temporary satellite data and used as input for decision making in geographic information systems. Evaporative structures are among the geological formations that are geographically expansive in our country, including Zagros china. One of the phenomena of the morphological index associated with this evaporation structure is the structural development of salt domes. The study of salt domes due to the unique properties of salt in terms of tectonic and lithology and strong interactions between motor and thermal flows is of great importance in geology. In this study, the artificial neural skull method and Landsat 8 satellite imagery were used to classify and prepare a global salt dome map.

As one of the global dilemmas that inflicts heavy human, financial and economic losses on an annual basis, study of mass movement has special importance, particularly with the increase in population and settlements over steep slopes prone to mass movement. International statistics related to human and financial losses caused by this phenomenon are steadily increasing. Frequent landslide events, their daily expansion in many parts of Iran in recent years and their destructive effects have attracted greater interest in responsible authorities, especially landslide experts, than ever before. Identifying and zoning areas susceptible to landslides is necessary for reducing losses. Since preparation of landslide susceptibility maps substantially improves land use planning, it can serve as an efficient method for decreasing human and financial losses resulting from landslides. Correct and systematic landslide hazard zonation and factors influencing it can be useful and effective in making decisions for containment, control and reduction of losses caused by this phenomenon

The Abgalal Watershed is located in Khuzestan Province, southwestern Iran. It forms one of the sub-watersheds of the Zard River. The physical tools used in the study included the 1:100,000 geological map, the 1:50,000 topographic map, the 30 m digital elevation model and the precipitation data obtained from the Meteorological Organization. GIS was used to measure the shapes and geomorphological parameters. Fuzzy logic evaluates the probability a pixel would be assigned to fuzzy sets considering the fuzzy membership function. Fuzzy sets do not have clear boundaries and membership or non-membership in a specific fuzzy set is a gradual process. There are two common methods for defining fuzzy sets: in the form of a function or in numbers. In the former, the degree of membership is presented as a function and in the latter specific degrees of membership are assigned to discreet values.

Following preparation of the distribution map of landslide prone areas, the distribution of these areas was studied in the form of nine factors influencing landslide occurrence. Each information layer (elevation classes, slope, orientation of slope, distance from fault, distance from river, precipitation, land use and lithology) were classified into five categories each receiving a score of 1 to 5 based on degree of susceptibility to landslides. The category with highest degree of susceptibility to landslide received a score of 5. The factor maps were combined with the landslide distribution map to determine the relationship between landslides and factors influencing its occurrence and also to prepare the landslide hazard zoning map.

This research studied the landslide-prone areas in the Abgalal Watershed using a fuzzy logic model. Using field studies, geological and topographic maps, reviewing the previous research conducted on this subject, and also investigating the existing conditions in the study region, eight factors (elevation classes, slope, direction of slope, lithology, distance from fault, distance from river, land use and precipitation) were studied as the factors influencing landslide occurrence. Following the fuzzification stage, landslide zoning maps were prepared using fuzzy gamma operators (for gamma values equal to 0.7, 0.8 and 0.9). The results of the qualitative addition method revealed that the fuzzy gamma operator ( at gamma=0.9) was more suitable than the others. Finally, the obtained map was classified into the very high, high, moderate, low and very low susceptibility categories, with 1.6% and 94% of the study region in the high and very low susceptibility zones, respectively. These results demonstrated that the study region had high potential for landslide occurrence due to the presence of a river network, precipitation, rangelands, urban areas, and weak lithology. Moreover, large areas lying south of the study region also have high potential for sliding movement.

Among the types of environmental hazards, flood is one of the most destructive natural disasters that causes a lot of damage (Yousef et al., 2011). Floods are a common natural disaster in Iran after an earthquake that endangers human life. Every year, terrible floods occur in the northern cities of Iran, such as the provinces of Mazandaran, Gilan and Golestan. Due to the occurrence of these floods, the places with the highest potential for floods (sensitive areas) should be identified before planning by flood sensitivity maps (Bobek et al., 2012) to the loss of life and property (Kurgalas and Karatazas, 2015). In case of excessive rainfall, especially in the form of showers due to the uneven conditions and vegetation of the region, most of it becomes runoff and after a short time flows as a flood and causes a lot of damage. The increasing trend of floods in recent years indicates that most parts of the country are exposed to periodic and destructive floods and the extent of flood damage and loss of life and property has increased, which is one of the reasons for the frequent recurrence of this phenomenon in the result is a disturbance of the hydrological and ecological balance (Miller et al., 1990) such as urbanization (along rivers), climate change (Tehrani et al., A 2015; Kjildson, 2010) and deforestation (Branastirt, 2003).

The research method is applied based on the purpose and descriptive and analytical in nature. The method of data collection is based on library, field, spatial data of the study area and questionnaires. First, the study area is determined and the identification of the basin and the status of the factors are considered. In flood zoning, many factors must be considered, each of which is of varying degrees of importance, but due to the limitations that existed in the preparation of some layers and the limitations due to their length. There is a model process, the use of multiple layers of information causes excessive complexity of the model, cost and long time in model analysis and processing (Mousavi et al., 2016). Therefore, according to these limitations and previous experiences, the factors that have had the greatest impact on the occurrence of floods in the Gorganrood basin and are more in line with our models have been selected. In this study, 11 effective parameters in flood zoning including slope, slope direction, altitude, geology, rainfall, distance from waterway and river, distance from roads, distance from residential areas, drainage density, NDVI index (cover density) and runoff coefficient (including land use, soil texture, soil hydrological group, curve number, surface maintenance and annual runoff height) were prepared and classified. After identifying the effective layers, the steps of the research method are as follows:

Table 14 also shows the area of each flood susceptibility class. As it is known, 40% of Gorganrood basin per hectare has a very high and high sensitivity and as about 41% of the basin has a low and very low sensitivity to floods. Therefore, the results indicate a high risk of flooding in the Gorganrood watershed.

The results of this study with the results of Amir Ahmadi et al. (2011) in the city of Neishabour who concluded that among these effective factors in floods, the distance from the river and thewater way has the highest weight and impact and also the study of Sheikh et al. (2015) in Malaysia, agree that soil type has the least impact on floods. According to the final map, the high-risk areas are mainly located in the flat and sloping areas located in the north and northwest of the study area, and the closer we get to the steep and mountainous areas, the greater the flood potential in the basin is reduced.Although slope is one of the most important factors in the occurrence of floods, it has the lowest weight in this study; This is because this research focuses on flood damage, not the factors that contribute to the flood itself. In other words, although the slope plays a role in causing floods, but the most damage has occurred in low-slope areas, because in these areas the water velocity decreases and flood accumulation occurs, and this is the case with the areas with the most damage due to the flood observed in it, it is coordinated. Also, Fernández and Lutz (2010) consider flood slope and poor maintenance of drainage canals as the most important causes of floods by preparing a flood risk map in two cities of Argentina. Other researchers have also considered other parameters important in the occurrence of floods, including Sani (2008) by examining the factors affecting the occurrence of floods in rivers in Nigeria and concluded that the annual rainfall has the highest weight and land cover the lowest weight and they have an impact on the occurrence of floods. Morley et al. (2012) examined the flood potential of the Arno River in Italy and concluded that urban development areas are more vulnerable to flooding.The results of the final flood risk map in Gorganrood basin show the fact that 19.97% of the basin has a very high risk situation, 19.98% has a high risk situation, 19.98% has a medium risk situation, 20.07% has a low risk situation, 20% have a very low risk of flooding. Therefore, the results indicate the high capacity of the basin in terms of flood risk, so there are many areas with high and high risk, which need protection and watershed management measures (such as: prevention of soil erosion and destruction, reducing water sediment load, reducing the speed and intensity of runoff flow, increasing flood concentration time, creating opportunities for water infiltration in the lower layers of the basin and feeding the aquifers, planting plants suitable for the geographical conditions of the slopes and rehabilitating rangelands and creating green spaces in the basin.

In the current situation, although it is not possible to form glaciers in many heights of Iran, it must be acknowledged that exploiting the potential of water and soil is impossible without considering the climatic processes of the fourth period. Therefore, geoscientists, especially in the last two decades, have tried to estimate the height of the border snow and the water and ice balance line of the Quaternary Iran as accurately as possible in order to be more successful in presenting land management plans.To. Reconstruction of past climatic conditions takes place according to glacial Fermi phenomena and their relationship with the height of the equilibrium line (ELAs). The height of the equilibrium line of present and ancient glaciers is one of the parameters that is used as an indicator of climate change.

At the altitudes of the study area according to glacial geomorphic evidence such as; Existing circuses, alluvial fans, various terminuses and traces of old lakes, 81 glacial sub-basins were identified within these altitudes. To determine the exact boundaries of the glacial basins, the hydrological map of the study area was made based on Staller classification from the DEM map. According to the height of these terminals, the exact height of the permanent snow line has been determined using the Lewis and Hofer methods. Also, using the circus floor method (Porter), the amount of past ELA was reconstructed at these heights. Reconstruction of past temperature and precipitation conditions and estimation of current line equilibrium height (ELA) using climatic data based on average annual temperature and precipitation data of 11 synoptic meteorological stations and based on the year of their establishment in different periods Took.

According to the above figure and the obtained linear relationship between the average annual temperature and the height of ELA meteorological stations, currently 4629 meters were calculated in the study area. Comparing the past ELA with the present, we can see that the ELA line has changed a lot and is now much higher due to the warmer weather. According to the regression equation obtained between the average annual temperature and the height of synoptic and ELA stations obtained in the present and past, the adiabatic drop of the current temperature of the study area with a decrease of approximately 5.89 degrees per thousand meters. Estimated. Due to the adiabatic drop in temperature in the region during the rule of glaciers, the air temperature was 6.54 degrees colder at altitudes and the air temperature in the highlands was 9.86 degrees colder than now. Among these mountainous units, the highest ELA has been reconstructed using the Louise method at an altitude of 4063 meters and using the Hofer method at an altitude of 3875 meters at an altitude of one thousand and below the Rusk Glacier Basin. Also, the lowest ELA was reconstructed using the Louise method at an altitude of 2858 meters and using the Hofer method at an altitude of 2682 meters at the Jupar heights and below the Agharzi glacial basin. Reconstruction of the highest ELA according to Porter method at the heights of Hezar at an altitude of 3518 meters and the lowest at the heights of Barez with a height of 2953 meters The highest height of the glacial circus floor below the Sirch basin is at the heights of Plovar with a height of 3,703.2 meters and the lowest height of the circus floor below the difference basin is at the height of Jabal Barez with a height of 2912 meters.

In general, the ELA level in the northern slopes in the study area is lower than the ELA level in the southern slopes of these heights. Among these mountainous units, the highest ELA has been reconstructed using the Louise method at an altitude of 4063 meters and using the Hofer method at an altitude of 3875 meters at an altitude of one thousand and below the Rusk Glacier Basin. Also, the lowest ELA was reconstructed using the Louise method at an altitude of 2858 meters and using the Hofer method at an altitude of 2682 meters at the Jopar heights and below the Agharzi glacial basin. Reconstruction of the highest ELA according to Porter method at the heights of Hezar at an altitude of 3518 meters and the lowest at the heights of Barez with a height of 2953 meters The highest height of the glacial circus floor below the Sirch basin is at the heights of Plovar with a height of 3,703.2 meters and the lowest height of the circus floor below the difference basin is at the height of Jabal Barez with a height of 2912 meters.

In our country, the climatic conditions are such that in 65% of it, the average annual rainfall is less than 130 mm. Therefore, it has been facing water deficit for a long time. Therefore, recognizing the factors affecting the quality of water resources in these areas is important for protection in order to reduce the vulnerability of these resources. Among the various factors, material and type of rocks are more important in changing the quality of groundwater. On the other hand, due to the involvement of different processes and factors on different lithologies, special landforms appear on the surface of the earth, which, after identification, are useful for showing sensitive areas of the landscape and can be further examined.Therefore, in this research, we try to select Izadkhast basin as a sample of inland Zagros basins that have good but saline water resources. Efficiency of morphometric indices using GWR model to determine the sources of pollution of these waters, or In other words, determine the areas that receive the most impact from a particular formation, and investigation the effects of geology on the salinity spatial distributions of groundwater resources.

First, to identify the sources of salinity, the most sensitive areas to erosion had to be identified, so to achieve the goal, the drainage network of the basin as a landform was extracted from the topographic map of 1:50000 areas. Then, in order to study the differences of landforms in different formations of the region, the hydrographic and physiographic characteristics of the basin were studied that to better study these indicators using the DEM of 12.5 meters in the environment of IDRISI TerrSet 2020 software, the region was divided into 522 sub-basins. After that, the sub-basins layer was placed on the geological layer of the region and cut based on the formations in the region, and for each polygon, area, environment, compaction coefficient, drainage density and drainage texture were evaluated.In the next step, to select the best index, in consideration for the strength of the formations, each of them was selected as the best index using the cluster diagram, classification and using Bakhtiari formation as the validation index. Which has a cluster diagram, field visits and Google Earth image, the drainage texture index was selected as the best indicators for landform analysis.In the next step, multivariate regression methods of OLS and GWR were used to investigate the effects of formations on salinity expansion and their spatial changes to identify sensitive points, relationships between water qualityparameters and the desired index in each formation. So that the water quality data related to 14 observation wells in 2010 (due to more complete data) which among the 16 quality parameter data, after examining the relationship between the parameters, the ones that had the most significant correlation and relationship with the EC parameter, as a variable Dependencies and values of drainage tissue index in the formations in each of the polygons were used as an independent variable for statistical analysis.Finally, according to the significant relationships of water quality parameters with the formations determined in the OLS model and according to the degree of correlation of water quality parameters with drainage tissue index in each polygon in the GWR model, water quality degradation points and salinity producing areas for management, were identified.

Drainage Texture index has completely determined the resistance of different lithologies in the sub-basins in some formations, such as Lahbari, which consists of two types of conglomerate and marl lithology, It is easy to determine the type of rock and even in nonexistence of geological maps with acceptable scale can be used to distinguish the type of rock resistance from topographic maps with existing scales and even to identify lithologies using larger scale topographic maps by extracting drainage network as landform Used drainage tissue index.According to the maps, the highest correlation was related to the sodium, potassium and electrical conductivity parameters and the lowest value was related to the sulfate parameter, while the other parameters also showed a very high correlation with independent variables. In most qualitative parameters such as sodium, potassium, chlorine and electrical conductivity, the highest correlation is related to the west of the basin, which indicates the high impact of the salt diapir in the west of the basin on water resources, and wells that are close to these points have lower quality than wells in higher and farther points.Also, the results of GWR model are directly related to landforms in the region, so that Aghajari Formation and Mol, Champeh and Gori sections that have a significant share in changing the groundwater quality of the region are exposed in the center, north, northeast and northwest of the region as rough country and badland. These landforms, which are the most important forms of erosion in the region, occupy a relatively large part of the basin, which due to erosion, are one of the most important points in changing the quality of groundwater resources in the region.

The results show that the use of drainage tissue index as a characteristic of landforms in the region has easily identified sensitive and key points according to the degree of erosion and laxity and the use of this index in the GWR model shows that Hormoz, Champe, Gori, Mol, Aghajari and Razak are the most important destructive formations of water quality. Therefore, using drainage tissue index to investigate the factors affecting changes in water resources quality along with GWR model and its high power to model the location helps managers and planners helps to identify sensitive points of water resources destruction using surface landforms and to better manage to operate

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.

Subsidence is the collapse or subsidence of the earth's surface that occurs on a large scale for a variety of reasons. The 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. In Iranian subsidence, groundwater decline has been considered as the main factor, because there is a direct relationship between subsidence and the rate of groundwater decline in different regions. In fact, the prevailing arid climate in most of the interior 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. One of the areas that are exposed to subsidence is Hamedan-Bahar plain. The geographical and climatic location of these plains has caused them to have semi-arid conditions in terms of climate, and this has caused the amount of precipitation in this region to be much lower than in the western and northern regions. In these cases, this region is facing a shortage of surface water, and therefore the utilization of groundwater in this region is much higher than the amount of their nutrition. Therefore, one of the most important factors in over-exploitation of groundwater in the region is unfavorable climatic conditions. The trend of over-abstraction of groundwater in the region has caused the groundwater level of these plains to decline sharply in recent years, which has led to the risk of subsidence as the most important risk. Geomorphology in the plains of Hamedan province. Due to the importance of the issue, in this study, the subsidence status in the Hamedan-Bahar plain has been investigated using radar interferometry and SBAS time series method.

In this research, descriptive-analytical methods have been used to achieve the desired goals. Research data include Sentinel 1 radar images (73 radar images during the period 16/01/2015 to 14/01/20120), Landsat 8 satellite image (dated 13/06/2020 in order to prepare land use maps). The digital model is 30 m high SRTM, geological map 1: 100000 also information about piezometric wells in the area. Research tools also include GMT (for radar interference and SBAS time series), ENVI (for land use mapping), ARCGIS (for fuzzy logic and AHP), and expert choice (layer weighting). In the first stage, using the radar interferometry method and SBAS time series, the subsidence of the region has been evaluated and in the second stage using 6 parameters including slope, distance from river, type of land use, type of lithology, geomorphological units and groundwater decline and integrated model of fuzzy logic and hierarchical analysis (AHP), areas prone to subsidence have been identified.

In this research, after preparing the images, first the necessary preprocessions have been done on the images. After performing the preprocessions, based on the time base of the images, the desired pair of images has been selected for radar interference. After preparing the interferogram maps, a map of the subsidence rate of the area during the study period has been prepared. Based on the results, the study area during a period of 5 years (from 16/01/2015 to 14/01/2010), faced with 281 mm of subsidence, based on which it can be said that the study area It has 56 mm of annual subsidence. The results of the evaluations show that the maximum annual subsidence in the region with 95 mm was related to the years 2015 to 2016, and after that the amount of subsidence has been decreasing, and finally during 2018 to from 2019 and 2019 to 2020, it has been reduced to 42 mm per year. Also in this study, using 6 parameters, areas prone to subsidence have been identified. According to the prepared map, the middle areas of the study area, which includes the urban areas of Hamedan, Bahar, Lalejin and Salehabad, due to the type of use, type of lithology, low slope, location in the plain and alluvial fan unit, as well as high water resources Groundwater in these areas has a high potential for occurrence and intensification of subsidence in the future.

The results indicate that the Hamedan-Bahar plain has a great potential for subsidence so that according to the results of this plain during a period of 5 years (from 16/01/2015 to 14/01/20120), with 281 Mm of subsidence has been encountered, based on which it can be said that the study area has 56 mm of subsidence per year. Examination of the time trend of subsidence in the region shows that the maximum amount of annual subsidence in the region with 95 mm was related to the years 2015 to 2016, and after that the amount of subsidence has a decreasing trend, and finally during the years 2018. By 2019 and 2019 by 2020 it has been reduced to 42 mm per year. The study of the spatial trend of subsidence also indicates that the highest rate of subsidence has been in the downstream areas of Hamadan and the adjacent areas of Bahar and Salehabad. Also, the potential assessment results of subsidence-prone areas indicate that the middle areas of the study area, which includes the urban areas of Hamedan, Bahar, Lalejin and Salehabad, due to the type of use, type of lithology, low slope, location in the plain unit. The alluvial fan, as well as the large decline in groundwater resources in these areas, has a high potential for occurrence and intensification of subsidence in the future. Due to the fact that these areas had the highest rate of subsidence in calculating the subsidence of the region, so there is a correlation between the results of radar images and zoning

The sensitivity of some sedimentary rocks to dissolution and its importance in the characterization of rough terrain is due to the appearance of certain forms called karst.Approximately 25% of the world's population, especially in Asia, the Mediterranean and the United States, supplies their karstic aquifers. Since many areas and cities are dependent on karst water resources, karstic resources will be very important. In Iran, about 5 percent of the country is made up of karstic areas, which play an important role in the country's water supply. In addition to the importance and role they play in the supply of water, karstic water resources have the potential to be highly vulnerable, which has made them always vulnerable to pollution. Because karstic resources are the major sources of water supply, vulnerability assessment and risk zoning mapping is very important. Due to the importance of the subject, in this study, the karstic mass of Khorin in the high Zagros has been evaluated. In this research, first, using WLC model and network analysis (ANP), the areas of karst development in the area will be evaluated and then its vulnerability status will be assessed using Paprika model.

In this research, information and tools have been used to achieve the desired goals, including the 5-meter SRTM digital model (to provide elevation, slope, slope and river layers), Topographic map 1: 50000 (to check the topography of the area) and geological map 1: 100000 (check the area's geology). The tools used in the study also include ARCGIS (for final mapping and output) and IDRISI (for WLC model implementation). The analysis of the information in this study was conducted in two stages. In the first step, using 8 factors (lithology, distance from fault, distance from river, land use, slope, slope direction, elevation and climate) and WLC and ANP integrated model, karst development areas are identified, In the second stage, the Paprika model identifies the areas most vulnerable to pollution. In the third stage, the vulnerable areas are identified based on the results of the first and second stages.

Karst areas are of particular importance because of the supply of water. One of the issues with karstic resources is their vulnerability to pollution.Various studies have been conducted on the development of karstic resources and their vulnerability, most of which have evaluated this research either on the development of karstic areas or their vulnerability, But in this study, first, using the WLC and ANP models, the identified areas of karst development are identified. For this purpose, 7 lithology parameters, fault distance, river distance, land use, slope, slope direction, elevation and climate were used and then vulnerable areas are identified using the Paprika model. In the Paprika method, factors including the upper subsurface water content (P), type saturation (R), penetration (I) and karst waves (Ka) are used. The results of these methods indicate that the central areas of the study area have both high development potential and high potential for vulnerability.

Evaluation of the results indicates that much of the study area has great potential for the development of karstic resources, thus, 68/3 km 2 the study area is located at a very high potential for the development of karstic resources. Surveying the zoned map indicates that the central areas of the study area, located at the altitudes of the Khorin massif, have great potential for the development of karstic resources. In fact, because of their good lithology, high altitude and low slope as well as the type of cover, these areas have great potential for developing karstic resources. From the center towards the Khorin massif, the potential for karstic resource development due to reduced altitude, change in lithology type and low density cover is also reduced. The results of the Paprika model indicate that the contamination potential is higher in the central part of the karstic mass and that the amount of contamination and vulnerability decreases as we go from center to margin. In this study, in order to identify areas susceptible to pollution, the most susceptible classes of development are identified as vulnerable areas. Evaluation of the floor area indicates that the vulnerable class based on the Paprika model has a size of 35.3 km2, the floor has a high potential for karst development of 68.6 km 2 and also areas with high potential for karst development and vulnerability based on the Paprika model, is 22.7 km 2.

In recent years, the ideas of urban geological heritage, urban geomorphological heritage, and urban geotourism have become increasingly important among scientists and specialists. Although cities often included the perspectives created by artificial elements (buildings, roads, bridges, etc.), they often have unique geological and geomorphological phenomena; but urban development and the construction of infrastructural structures affect the urban land and sometimes completely destroy the landforms. Therefore urban geomorphological heritage is prone to degradation due to human activities related to urban development and the creation of infrastructure. To understand the effect of urban development on geomorphological heritage and the need to conservation this valuable heritage, the present study was conducted in Khorramabad, Iran. Khorramabad is one of the few cities that despite having great geomorphological potential for attracting tourists and developing the city's geotourism industry, because of lack of study and proper planning remains in isolation. According to preliminary studies, Khorramabad city has a special topography and valuable geomorphic heritage that is needs to be recognized, inventory and survey. Therefore, the main goal of this research is accurate inventory of geomorphological heritage of the Khorramabad city boundary and its surroundings to introduce a valuable collection to tourists and researchers and also an analysis of the importance of consernation this valuable heritage for the urban community. Furthermore, investigating the effect of urbanization growth, city development and physical expansion and human activities on the geomorphological heritage of the city of Khorramabad is the other objectives of this research.

Research data is obtained through fieldwork, thematic maps and available visual resources. This research has been conducted with an innovative approach through the integration of fieldwork, remote sensing and quantitative assessment. In the first instance, the geomorphological heritage of Khorramabad urban area has been important in several stages of fieldwork. In the next stage, the amount of urban development in Khorramabad during the last three decades has been extracted through satellite images and its effects on geomorphological heritage have been investigated. Finally, the degradation risk of each geomorphological heritages in Khorramabad based on the Brilha (2016) degradation risk index has been determined.

In urban environments, two categories of geomorphic heritage have been known so far. 1- Each geomorphosite (a landform that defined as a heritage by a scientific community) located in urban spaces, such as waterfalls, caves, moraine and etc. 2- A specific place that helps to mutual understand between geomorphology and urban development. According to the results obtained from the inventory of geomorphosits, 33 typical geomorphosites were inventory in the boundary of Khorramabad city and surrounding it. These geomorphosites can be classified into four main categories of karstic geomorphosites (such as caves, springs and karstic mirages, natural vault, karens), rivers (such as Robat rivers, Karganeh and Khoramabad, golden waterfall, close of Shabikhun), tectonics (such as Makhmalkooh, Khorramabad roof), and anthropogenic or human-made (such as keeyow lake, hill of Sangar mahi bazan and hill of Masur). Khorramabad city has been influenced geomorphologically and linearly along the Khorramabad River. In general, the residential area of Khorramabad has more than tripled from 1990 to 2018; that during it also twelve geomorphosites have been in residential area because of human activities. So with the city's physical development process, increasing population pressures and easier access of human societies to urban geomorphologic heritage, road construction and construction of passages, establishing of inharmonic and unplanned promenades and also the process of smoothing and excavating mountainous regions to create residential homes often illegal in downhill, disposal of garbage and municipal wastes, use and exploits of private; and following it lack of a synchronized program with this fast development, to create buffer zone for conservation and legal prohibition to prevent of destruction of these geomorphosites, has led to increasing pressure and as a result destroy more than before within the geomorphocytes range.

The results showed that the urban area of Khorramabad increased about three times during the period from 1990 to 2018. During this time, many of the geomorphological heritage works were destroyed or its major values (aesthetics, science, education, tourism, etc.) they are removed. The results of degradation risk assessment also showed that geomorphosites of Makhmalkooh, golden waterfall, Konji cave. due to the rapid growth of residential development, uncontrolled or even illegal, they are exposed to the greatest damage caused by human activities, including construction, clearing and road construction, waste disposal, private use and exploitation, and so on. Accordingly, it is necessary to the allocation of conservation for them, including the determination of the legal core zone and buffer zone.