ehsan ghaleh

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,

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.

 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.

Introduction:

 Soil degradation by water is the most serious form of land degradation in many parts of the world, especially in arid and semi-arid areas, where soil formation rates are usually less than soil degradation due to rapid soil erosion, the impact of human abuses And incorrect use of soils. For this reason, crushing land control strategies such as agricultural agriculture, mulch, environmental improvement or land expansion are necessary to avoid drought in agricultural land. Awareness of the process of soil erosion and sediment transport as an effective factor in reducing soil fertility and soil loss, filling dams, catching and blocking irrigation channels, polluting water from rivers, and reducing water quality have long been considered by geoscience experts. Understanding the factors affecting sediment production plays an important role in determining the amount of sediment yield of a basin and understanding the phenomenon of erosion and its consequences and can be used to prioritize sub basins in a watershed. Areal characteristics encompass morphological characteristics such as drainage density, stream frequency and watershed shape parameters. Ease access to Digital Elevation Models, remote sensing data as sediment yield predictors, simplify the calculation of the watershed geomorphic characteristics. The purpose of this study was to use the geographic information system to extract the watershed geomorphic characteristics and determine their relationship with sedimentation in the Gharanghoo basin.

Methodology:

This study was conducted in 19 subwatersheds in Gharanghoo basin. In order to select appropriate subwatersheds, the hydrometric and rainfall data for hydrometric and meteorological stations were obtained from East Azarbaijan Regional Water authority for the selected watersheds. Annual sediment load was calculated using sediment rating curve method. Physiographic and geomorphic characteristics including 25 geomorphic parameters were calculated for each sub watershed using digital elevation model with spatial resolution of 30 m. In order to determine the relationship between geomorphic characteristics and sediment yield of the subwatersheds, a multivariate regression stepwise analysis was used. In the multivariate regression, the important geomorphic characteristics which affect watershed sedimentation are identified and based on those parameters, the best annual sediment yield and geomorphic characteristics equation were presented.

 Results:

 The annual amount of sediment varies from 63500 tons per year in the Kalghan sub basin (Kalghan dam) to 4636762.6 tons per year in the gharanghoo area at the intersection with Ghezel Ozan. Basin sedimentation weight as dependent variable and other parameters were considered as independent variables. The variables of flow volume, area, environment, equivalent rectangular length, equivalent rectangular width, drainage density, branching index, minimum height, coefficient of elongation and roughness of the basin were compared. Other variables have higher correlation with sediment yield. The result of the study of the relationship between geomorphic characteristics and sediment of sub-basins showed that the amount of sediment produced with flow volume and basin coefficient was positively correlated and was significant at 5% level. The principal components analysis (PCA) method was used to identify the factors affecting sediment yield of the existing variables. The results show that the four factors of area, area, length and coefficient of form of basin are 50, 20.9, 13.6 and 7.7 percent of the variance of all variables, respectively. In total, the four finalized factors have been able to explain 92.2% of the variance of all research variables.The results show that the four factors of area, area, length and coefficient of form of basin are 50, 20.9, 13.6 and 7.7 percent of the variance of all variables, respectively. In total, the four finalized factors have been able to explain 92.2% of the variance of all research variables.

Discussion & Conclusions:

 The results of this study indicate that there is a significant relationship between the geomorphic characteristics of the studied watersheds and annual sediment yield. Watershed Form factor is a dimensionless index for flood flow and movement, erosion severity and sediment transport capacity of watersheds. This factor is a function of watershed area and length. Run off and amount of flood peak in bigger watersheds will increase sediment yield. Many researches have reported high correlation between rainfall and sediment yield. Arid climate and poor vegetation cover in selected watersheds is the main reason for high correlation of rainfall and sediment yield. Soil erosion and sediment yield will increase due to high intensity and low duration of rainfall along with scarcity of vegetation cover and erodible soils in this region. Overall, study results indicated that with the development of new technologies and the possibility of extracting different physiographic and geomorphic parameters of watersheds from a digital elevation model, it is possible to present regional equations for prediction of sediment yield using geomorphic characteristics that can be used in sediment control and Watershed Management Programs.