کورش شیرانی

Soil erosion is one of the most important environmental issues in the world; so that, soils that are affected by degradation processes such as compaction, loss of nutrients and water retention capacity lead to soil erosion and loss in fertile agricultural lands. Gully erosion is the most severe type of water erosion, which is one of the main factors of land degradation. Spatial forecasting of gully erosion using models that are based on land sensitivity to gully erosion and their output leads to the preparation of gully erosion risk maps in the form of gully erosion is the most suitable strategy for land management planning in watersheds to prevent gully erosion. If there is a zoning map of gully erosion in watersheds, economic and social damages could be controlled through appropriate measures and strategies in addition to preventing heavy costs to control gully erosion. According to the available models and relationships, it can be concluded that the most efforts in gully erosion modeling have been focused on the location of gullies as well as predicting the possibility or non-possibility of gully occurrence; So that, only a part of the effective factors have been investigated due to variability of factors affecting the gully. While the present research aims to determine the most important factors affecting the gully erosion and prioritize the factors and finally zoning the gully points and sensitivity to the gully in Maroon watershed.Maroon watershed is located in the southern and southwestern slopes of the Middle Zagros. The area of the watershed is 3837 km2, the maximum height from the sea level is 3420.3 m and the minimum is 372.8 m. The average annual rainfall is 815 mm, the long-term average annual temperature in te representative stations of the plains is and the stations located in the highlands are 19 and 16 ºC respectively. According to the Dumarten system, the climate type is Mediterranean to semi-arid.In summary, the present research was carried out as follows:  1) Selecting the area, preparation of the distribution map of gully occurrence (dependent variable) and its random division into two training or calibration (70 percent) and experimental or forecasting (30 percent) categories, 2) preparation of the maps of 23 effective factors (independent variables), 3) selecting the effective factors using alignment test between the effective factors and the occurrence of gullies, 4) running the model, 5) validation and evaluation of the model, and 6) preparation of a zoning map of susceptibility to gully erosion.In the present study, the location of gullies created in the Takht Daraz watershed was first recorded by field visit, and based on the recorded points, they were divided into two groups of training data (70%) and experimental data (30%) respectively in order to calibrate and validate the models. 23 factors affecting the occurrence of gully erosion were identified based on scientific references and watershed conditions, and their maps were prepared in ARCGIS environment. The weight of these factors was determined based on the frequency ratio, and then the correlation of the effective factors was investigated using the collinearity test. In the next step, the maximum entropy model was calibrated and validated using the weighted data of the effective factors and the spatial training and experimental data of the gully distribution. The Jackknife test and the receiver operating characteristic index were respectively used to determine the thresholds of effective factors in the occurrence of gully erosion as well as evaluating the efficiency of the studied models.

According to the collinearity analysis, the variance inflation rate of the investigated variables was less than 0.5, which indicates the non-collinearity between the investigated variables and all the variables were included in the modeling process. Based on the results of MaxEnt model (maximum entropy), a gully erosion sensitivity map was obtained, based on which the very high sensitivity class with 52 percent of gully occurrences has the highest density of gullies; So that the ratio of the frequency of gullies in this class was 6.73 and the Seed Cell Index area was 0.15. The very low sensitivity class has the lowest amount and frequency ratio of gully by 0.02 and nuclear cell surface index at the rate of 46.8. According to the information obtained from the map, the highest percentage of the area is related to very low sensitivity class (46%), which has eight pixels of gullies, and the lowest area of the watershed is related to the very high sensitivity class (8%) which has 420 pixels of gully. In general, based on the maximum entropy method, 34% of the watershed is in the medium to very high sensitivity class. Also, the validation results of the maximum entropy model based on the index of the area under the curve (AUC) showed that the area under the curve in this method was 0.85, which indicates the high accuracy of the maximum entropy model.

In the present study, the spatial distribution of gullies and its relationship with the effective environmental parameters of the Takht Daraz basin have been used in order to evaluate the sensitivity of gully erosion. For this purpose, different methods and models including witness weight, belief model and entropy method have been used to prepare a gully erosion sensitivity map. Google Earth image interpretation, past research and extensive field visits were used to prepare the gully distribution map, and 70% of the gullies were used for modeling and 30% for validation. In the field of effective parameters in gully erosion, 23 effective parameters were identified and prepared using research literature, access to data and environmental conditions of the study area. The results showed that the entropy model with the area under the curve (AUC) of 0.86 had high prediction accuracy. The results of the effective factors on gully erosion showed that according to the Jackknife test, the lithological factor had the greatest effect on the areas under the curve. The results of determining the parameters weight also showed that different parameters have different participation rates in the gully erosion occurrence; so that, the parameters of land-use and geology had the largest share of participation in the occurrence of gully erosion, with a contribution of 24% and 18% respectively.

Gully erosion is one of the advanced forms of soil erosion, which needs to be analyzed and identified in order to protect the soil. In this research, according to the complex system of factors influencing the creation of ditch erosion, 23 factors were analyzed in the two famous Dempster-Schiffer models and the entropy model, and using Google Earth images and field visits, 331 ditch points were identified, recorded, and a ditch distribution map was prepared. Spatial data of gully erosion distribution were divided into two random training (70%) and experimental (30%) groups. In this research, two indicators of tolerance coefficient and variance inflation factor were used to check the collinearity test, and as a result, two indicators of waterway density and relative humidity index were removed and 21 factors were used in the modeling process. The output results of the layers, weighting and classification and integration in two Dempster-Schiffer and entropy models are the extraction of the zoning map of the gully's erodibility sensitivity. and 30% of the calibration and validation of the models, the area under the ROC system performance characteristic curve and the area under the AUC diagram of the Dempster-Schiffer model with an explanation factor of 0.934 and the maximum entropy model with an explanation factor of 0.936, both models have an acceptable percentage of the area under the curve were that this issue shows the high performance of both models in the region. Among other results of statistical analysis, the prioritization of the impact of 21 factors in causing ditch erosion in the region was determined. The scientific results of the research can be promoted and taught, and from the practical point of view, the relevant executive body to control ditch erosion can take the necessary measures using the results of this research.

In recent years, problems caused by human interference in the Salmas Plain, such as an increase in the irrational use of surface water resources, an increase in the process of groundwater discharge, and a change in land use have caused pressure on the groundwater resources of the region; hence, land subsidence is at least a natural response to the decrease in groundwater level. Therefore, it is important to study and identify subsidence areas in the region.

In this research, groundwater information and Sentinel 1 radar images were used. Groundwater information was obtained from the regional water company of West Azarbaijan province. In the present research, the DInSAR algorithm was used to measure the ground surface displacement, and the phase difference of the SAR signals was determined using the repeated-pass interference methods. Finally, by using the manual of differential radar interferometry with synthetic aperture and 52 even numbers of Sentinel 1 image, the subsidence in the Salmas Plain was determined between 2014 and 2015 and 2018 and 2019. Validation of the radar interferometry method using Qareqeshlaq Geodynamic station data, as well as groundwater level changes, underground water drop and field observations was conducted.

The results of the subsidence study using the DInSAR method showed that the average annual displacement rate is about 5 cm. Investigation of groundwater changes also indicated a decreasing trend in most of the wells. The wells located in the eastern and southeastern regions (e.g., Kangarlu, Qareqeshlaq, Yushanlu, etc.) have the highest amount of groundwater drop. In the study period (2014 to 2019), where the wells with an increase in groundwater level were affected by their proximity to the Zola dam reservoir and the water supply channels. The wells of Qezeljeh, East Qareqeshlaq, and Maidan Doab have the highest water level reductions, but they do not have sufficient water supply from the dam. Mahlam, West Tazeshahr, and Ian wells, which are in the western part of the plain and close to the dam lake, respectively, have a suitable increase. Checking the subsidence maps of the Salmas plain showed that most of the subsidence is concentrated in the southern areas, especially in the southeast of the plain, in comparison to other parts of the plain the depth of alluvium is also greater in these areas. This subsidence trend continued from 2014 to 2019 and this trend is also prevalent in the final map. During this period, the average water level changes in the Salmas plain aquifer had a downward trend, which was in harmony with land subsidence. By reviewing the groundwater drop map, it was found that the areas with the highest drop coincided with the subsidence areas. More drop in underground water indicated more water extraction. Therefore, the discharge of underground water in the region has caused subsidence.

Before adopting any operational plan and policy for implementation, it is the most important task to identify the exact location of land subsidence in any area, in particular residential areas. Sentinel1 radar images are sufficiently capable of solving this problem at all stages; however, a longer observation time would be more useful for decision making and implementation. Land subsidence in the Salmas plain is fully confirmed by the change of the underground water level and field visits. According to the DInSAR approach, the average displacement rate due to subsidence in the region was 5 cm, which can be fully verified in terms of observations of changes in the groundwater level. The piezometric data show a reduction of in the water level in most wells compared to the past, which depicts the occurrence of excessive extraction of groundwater. In addition, field surveys have been conducted in different areas of the Salmas Plain and the subsidence areas have been recorded. The process of subsidence in the studied area is ongoing, and the attention and care of the relevant authorities is needed to reduce the damage caused by this phenomenon and adjust the possible damages. It is recommended that people should be aware of the consequences of excessive harvesting and recommend alternative crops with a low annual demand for water and early yields.

Gully erosion is one of the factors threatening the environmental balance and its stability and plays a prominent role in increasing the sedimentation capacity of watersheds and changing the hydrological characteristics of surface streams. The purpose of this research is to evaluate the effectiveness of the maximum disorder machine learning model in order to prepare a gully erosion sensitivity map in the Semnan watershed. Identifying the most important environmental factors affecting the occurrence of gully erosion using the jackknife method and examining the importance of each environmental factor in the study area by analyzing the response curves are other goals of this research.

This research consist of five main steps: the first step is choosing the study area, collecting and preparing maps of the effective factors, the second step is preparing the distribution map of the ditch erosion event, the third step is the multiple collinearity test with the index of the tolerance coefficient and the variance inflation factor in order to check the information overlap of the effective factors. and checking the importance of the factors, the fourth step is to implement the maximum irregularity or maximum irregularity model, prepare a zoning map of sensitivity to the event of gully erosion and classify it into five categories: very low, low, medium, high and very high, the fifth step is to evaluate the classification accuracy and validate the zoning map and Prediction of sensitivity to gully erosion.

According to the results, among the 23 initial factors or variables, the factors of the waterway slope length index, surface curvature, and waterway power index had colinearity or overlapping information, and in the next stages, they were removed from entering the modeling process, and modeling was carried out with 20 independent factors or variables. The gully erosion sensitivity map of the studied area showed that the southern and southern parts of the Semnan watershed are prone to gully erosion. Also, the zoning results obtained from the implementation of the maximum irregularity model indicate that the areas prone to gully erosion are in high areas, sensitive rock units (quaternary clay and marl zones), average annual rainfall, Eridesville soil type, ultra-arid climate, high drainage density classes, low slope, low lands without geographical slope, high topographic moisture index, pasture land use and low land surface texture are observed. Also, the maximum irregularity model has 91% and 89% accuracy in the calibration and validation stages, respectively, which in terms of efficiency is in the very good category for predicting gully erosion prone areas.

About 35% of the area of high and very high sensitivity areas resulting from the implementation of the maximum irregularity model include more than 90% of the ditches in the region. Also, the maximum irregularity model in the stages of implementation or development and prediction or validation with the area under the receiver operating characteristic curve with the values of 0.91 and 0.89, respectively, is effective in zoning and predicting the occurrence of gully erosion. This result will be useful for local managers and planners as well as executive experts in order to identify areas prone to gully erosion and determine the best implementation methods of watershed operations for soil protection approaches.

Khanmirza plain is one of the plains of cold and temperate regions of Iran, with an average rainfall of 500 mm per year and sufficient nutrition in terms of surface resources, the subsidence event is currently happening in it with a rapid trend. In terms of geostructure, Khanmirza plain is located in the folded Zagros zone in the south of the Dana fault, and the presence of piezometers protruding from the soil, as well as the presence of springs, etc., are signs of crustal movement on the surface of this plain. For this purpose, with the aim of evaluating the tectonic activity of the region in creating the phenomenon of subsidence from some geomorphic indices such as basin shape index (BS), river longitudinal gradient (SL), drainage basin asymmetry (AF), reverse topography symmetry index (T), integral index Hypsometry (H) of the region's subsidence was studied tectonically. Likewise, the subsidence rate was determined using radar interferometry. Examining these indicators showed that the indicators of basin shape (BS), river longitudinal gradient (SL), drainage basin asymmetry (AF), reverse topography symmetry index (T) are active tectonics in the region. Earth construction activities have caused the plain to stretch and become asymmetric on both sides of the main river. Likewise, the results of radar interferometry in the two-year period indicate that parallel to subsidence (at a rate of 9 cm) in some parts of the plain, other parts are facing uplift (at a rate of 21 cm).Collapse and subsidence can be caused by natural geological phenomena such as earthquakes, dissolution of limestone, chalk and salt rocks, melting of ice and compaction of deposits, slow movements of the crust and the release of lava from the solid crust of the earth, or activities human activities such as mining, extracting underground fluids such as underground water, oil or gas (Galloy and Barbie, 2011: 1459). Radar interferometry has been widely used by Earth researchers since the early 1990s (Shirani et al.: 2013). This technique can be used to measure displacements resulting from many phenomena such as earthquakes, landslides and subsidence. In general, there are nearly six hundred plains in Iran, and it is likely that more than half of them are subject to subsidence (Kia Sharifi, 2010: 58).

In order to evaluate the tectonic situation of the region, from topographic maps 1:50000 and geological map 1:100000 to calculate basin shape index (BS), river longitudinal gradient (SL), drainage basin asymmetry (AF), symmetry index Inverted topography (T), hypsometric integral index (H) from (DEM) 30 meters and topographic map were used, which were processed in GIS environment. D-InSAR radar images were used in the period from 2003 to 2005, which were processed in ENVI 5.3 software. Radar interferometry technique

How to calculate the SL river gradient index: The SL index is calculated for the middle point between two flow curves. Symmetry index of transverse topography (T): this index can also specify the state of symmetry and, as a result, the active or inactive state of the region. Drainage basin asymmetry index (AF): The asymmetry index is a method to evaluate the existence of tilts caused by tectonic activities at the scale of the drainage basin. Basin Shape Ratio Index (BS): Basins that have an elongated shape are tectonically active. And the shape of basins that are tectonically inactive tends to be round. Integral Hypsometric Index: Integral Hypsometric is an index that calculates the height of the entire area in relation to the area of the entire area. The values of this index show the transformation stages of the landscape in the erosion cycle. Today, radar interferometry, as a technique that estimates the displacement of the earth's surface with high accuracy and resolution, is a common tool for investigating the change of the earth's surface due to various factors, including land subsidence. In this technique, an interferogram can be prepared using two satellite images. So that by using the interferometric tool, the phases of the return signal from the ground are subtracted from each other in two time-delayed satellite images of the same region to extract changes in the ground surface. The radar interferometry technique uses Sarscape software as one of the powerful tools in the digitization of satellite images to survey the changes in the earth's surface. In this research, 12 Envisat satellite images from the European Space Agency were prepared

 The amount of subsidence calculated from 10 paired images is 9 cm drop in a two-year period, and the amount of elevation obtained from the images was calculated as 21 cm in a two-year period. By comparing these two numbers, it can be concluded that in this basin, the uplift rate is twice as high as the subsidence rate.The obtained values show that in addition to groundwater extraction as one of the main and common causes of subsidence, considering the tectonic factor in areas such as the Khanmirza watershed with high rainfall input is one of the main reasons for its formation.

Floods and earthquakes are the most common and unpredictable natural events that harm communities by affecting life and property and affect the landforms of the basin. Therefore, in ways to reduce the effects of natural events, The main goal has changed to public awareness and emphasis on informing the people in line with preventive measures before the occurrence of abnormal events. Considering these explanations and the need to compare and evaluate the effectiveness of probabilistic statistical methods, The main goal of this research is to investigate the morphological, hydrological and climatic factors affecting the occurrence of floods, Modeling and evaluation of land sensitivity potential in Gorganrood watershed to flood occurrence. Also, the most important innovation of the current research compared with to the previous researches related to risk zonation in the country, the comparison of two probabilistic methods of Shannon entropy and weight of evidence in order to determine the most efficient model and increase the accuracy of flood prediction maps.

1- Selection of the study area, Collecting and preparing maps of effective factors as independent variables, 15 factors were considered in this research. 2- The flood event distribution map was prepared using the information received from the Ministry of Energy and field observations as a dependent variable, and it was divided into two groups of test points (30%) and training points (70%). 3- Quantifying the effective factors with the help of the frequency ratio index and also performing the multilinear test (independence of the effective factors). 4- Implementation of entropy and weight of evidence models, Preparation of flood sensitivity zoning maps and classifying them into five categories: very low, low, medium, high and very high. 5- Assessing the accuracy of classification and validation of zoning maps and predicting sensitivity to flood events. 6- Determining the importance of effective factors and choosing the most suitable model.

In total, based on the calculated weights of both entropy index and weight of evidence models, in the order of factors land use, altitude, Normalized difference vegetation index (NDVI), soil, Topographic Wetness Index (TWI), Drainage density, Stream power index, slope (SPI), Topographic position index (TPI), lithology, average annual precipitation, Infiltration number, Profile curvature, Stream frequency and Intensity of daily rainfall are effective in flood occurrence. In this research, the land use factor and then the vegetation cover factor have a high priority in the occurrence of floods due to land use changes. The intensity of rainfall is in the last rank, but its importance and influence should not be ignored along with other factors. The maps resulting from flood risk zonation by both models indicate the high accuracy of zone classification and proper distribution of flood points in high and very high zone. Also, based on the receiver operator characteristic (ROC) curve and accuracy classification levels, the efficiency of both models is in the very good category (0.8-0.9) In terms of comparing the models to each other, the results of the area under the curve (AUC) values of the Shannon entropy model show a higher score accuracy in both testing and training situations (0.94 and 0.98) than the Bayesian weighting method (83.83). 0 and 0.88) which indicates a high correlation between the prepared flood sensitivity map and the distribution map of the number of flood events.

Conclusion and Suggestions:

In this research, in order to prepare a map of flood event zoning, 368 flood zones with a repetition number of 782 flood events and 15 factors affecting flood events were used. Based on the fitting of both models and the prioritization of the factors affecting the occurrence of floods, it was determined altogether that land use, altitude and vegetation factors have importance and priority in the occurrence of floods in the study area and Profile curvature, Stream power index and Intensity of daily rainfall factors were placed in the last priorities. The results showed that Shannon's entropy model has the appropriate class threshold in classification, and by using 30% of flood occurrence zones, it has more priority for zonation and flood occurrence prediction. But in general, both models are effective in zoning and predicting flood occurrences.

Segzai plain, 40 kilometers from Isfahan city, with an area of about 40,000 ha, is considered a serious threat to this historical city. This plain, which until a few decades ago was a relatively prosperous reed and meadow, has now become a huge danger in terms of nature destruction and environmental pollution. Two natural and human factors play a role in the desertification of this region. Among the natural factors are low rainfall, high evaporation, the presence of limiting layers in the soil and strong winds and from human factors, excessive grazing and overgrazing of livestock as well as bush-cutting, rapid population growth and excessive exploitation of existing resources decline Underground water and most importantly, exploitation of surface mines, especially gypsum mines, can be mentioned. The main goal of this research was to evaluate the effectiveness of the SEBAL model for estimating the actual evaporation and transpiration of the Segazi Plain, considering the arid and semi-arid location of the region using the landsat 8 image. 

To do this research, first, landsat 8 images were processed. Extraction of required information from satellite images in this research was done during three main stages, i.e. pre-processing, processing and post-processing. In other words, in the pre-processing stage, after performing atmospheric, geometric and other necessary corrections, the image was referred to the ground. In the area of data processing, different highlighting methods and statistical analyzes and remote sensing were done in order to achieve the information layers of the plan. In order to evaluate the results in the image processing stage, the post-processing of the data based on various analyzes was used to evaluate the reliable layers in terms of accuracy and precision. After that, the SEBAL algorithm was implemented.  first the amount of net radiation (Rn) was calculated according to the temperature of the earth's surface and vegetation and the amount of energy reaching the earth, then the heat flux of the soil (G) was obtained to determine the amount of transfer capability The heat into the soil was determined, then it was determined to calculate the amount of sensible heat flux (H), which determines the loss of energy from the soil to space. Finally, after determining the sensible heat flux, evaporation and transpiration were calculated. The SEBAL algorithm calculates the energy balance equation in order to calculate the actual evaporation and transpiration of the plant. 

Surface albedo parameters (the highest and lowest weighted values are around 0.85 and 0.16), soil surface temperature (the highest and lowest weighted values are around 326 and 299 degrees Kelvin), NDVI vegetation index (the highest and lowest weight values related to areas with good vegetation close to +1 and related to water and water bodies close to -1), the amount of net energy reaching the surface of the earth (the highest and lowest weight values are about 703 and 210 Wm-2, soil heat flux (the highest and lowest weight values are about 130 and 35 Wm-2), sensible heat flux (the highest and lowest weight values are about 323 and 23 Wm-2 , momentary evaporation and transpiration (the highest and lowest weight values are about 0.842 and 0.225 mm) and daily transpiration evaporation (the highest and lowest weight values are about 20.2 and 5.4 mm) are among the most important effective parameters in this Sabal algorithm which were investigated in this research. Changes in actual transpiration evaporation (the highest weight values about 0.85 mm and the lowest weight values about 0.16 mm). The obtained results showed that the SEBAL model has well predicted evaporation and transpiration in areas that have vegetation, mostly agriculture and gardens, so that the amount of water loss through evaporation has been predicted close to the values found in the eastern synoptic station of Isfahan (airport Shahid Beheshti) is registered. 

The amount of error obtained in SEBAL calculation was 0.1%. The amount of real momentary evaporation and transpiration has been calculated in the range between 0.22 and 0.84 mm, according to the weather conditions of the region and the temperature of the air near the surface (27 to 50 degrees) and the amount of evaporation and transpiration recorded by the Penman-Monteith equation (30.0 mm in the east of Isfahan synoptic station), this value is in a reasonable range. Comparing the outputs of Sabal model with the amount of evaporation and transpiration obtained in the same station, which shows the root mean square error (RMSE) value of 0.1, indicates the suitability of this algorithm in calculating evaporation and transpiration in Segazi region. Considering the growing need of the country to prevent the wastage or excess consumption of water in the agricultural sector, either through changing the cultivation pattern or changing the irrigation methods, the application of the developed tool of the Sabal algorithm in this research can provide valuable information to the experts and managers of the water sector put agriculture. The results obtained from this implementation of this research showed that remote sensing has a good potential for estimating actual evapotranspiration (ETA) by having different algorithms such as SEBAL algorithm and minimum ground information.

Marl is a type of mixed carbonate–silicate sediment and contains clay and carbonate minerals deposited in different environments. The terms marl and marlstone are still imprecisely used in geology. I n this study, to revise the nomenclature and sedimentary classification of marl deposits, 99 samples were taken from different terrestrial and marine marl formations. The texture and percentage of carbonate and salt contents in the marls were measured. According to Folk's classification, the majority of the samples are in the range of sandy silt, sandy mud, silt, muddy sand, and silty sand. Based on the electric conductivity parameter (EC), all samples have a high content of salt and are classified in saline and very saline classes. The results based on the classification method of Haldar and Tisljar also confirm that most of the samples are in the category of "calcite clayey siltstone", clayey calcite siltstone, and “calcite-silt clay” and a few examples are "calcite – silt clay” and clayey- silt limestone, except for one sample of the Kond Formation, which is within the marl field. The main samples are not classified by the Pettijohn method and samples are mostly silty, and muddy and the name chalk-salt siltstone and mudstone were found to be more suitable for the terrestrial marl deposits under investigation in this research.

Groundwater is always considered one of the important water resources, especially in arid and semi-arid regions of the world, such as Iran. In recent decades, it has decreased drastically due to excessive use. The objective of this study was to determine the best interpolation method and evaluation of the spatiotemporal variations for the groundwater level in the Sahneh-Biston plain of Kermanshah province during three decades from 1991 to 2020. At first, four Gaussian, linear, spherical, and power semi-variograms were obtained for observations. Then, the best semi-variogram and interpolation methods were selected among the evaluated methods for zoning the groundwater level in the region. The lowest value of the sum of RMSE, MBE, and MAE error criteria and the highest coefficient of determination (R2) between observations and estimates in all three decades and the average of the entire period were calculated and considered to evaluate the most appropriate semi-variogram and interpolation methods for spatial distribution. The results showed that the ordinary kriging method with Gaussian semi-variogram is the best method to estimate the groundwater level in the Sahneh-Biston plain. The average difference between the minimum and maximum groundwater levels based on the observation wells of the study area and the zonation method is from 1279 to 1372 meters and 1289 to 1409 meters during the studied period time, respectively. The groundwater level is placed in more depth with the proximity to the central and southern regions. The maximum decrease and increase of groundwater level variations have been 12 and 19 meters during three decades, respectively. Also, the underground water level variations during these three decades showed that both the second and third decades compared to the first decade and the third decade compared to the second decade have increased in more than 50% of the region. This increase can be caused by the optimum management and water use in these years. Therefore, groundwater level monitoring provides effective help for experts and users in planning and optimal management of groundwater for the sustainable development of water resources.

Analysis of statistical relationships and quantitative factors and parameters affecting the production of sediment and soil loss is one of the problems of the watershed. The purpose of this study is to model the relationship between sediment production rate using MPSIAC experimental model factors and observed sediment rate using geomorphic parameters and their relationship with sediment yield in the watershed. Rastamabad watershed of Ilam province was selected by a simple random method with four sub-watersheds identified and equipped with a hydrometric station from the southern basins of Ilam province. In this study, an experimental model was used to estimate the sediment yield. Flow and sediment data of four hydrometric stations and 12 meteorological stations from 1991 to 2020 for 30 years were prepared by the Regional Water Company and the General Meteorological Office of Ilam Province. Physiographic information of the sub-watersheds was calculated from topographic maps with a scale of 1.25000 and the geomorphic features of the sub-watershed were extracted from the digital elevation model. Using factor analysis and cluster analysis, influential factors and variables were identified and sub-domains were classified and divided into homogeneous regions. In order to investigate the correlation between independent and dependent variables, the data normality test was performed by Shapiro-Wilk and Kolmogorov-Smirnov tests in SPSS software. The statistical multiple regression method was used to analyze the relationship between experimental model factors and geomorphic variables with sediment yield of each watershed. The results showed that the sediment yield had a positive correlation with geological factors, land use, upland erosion, river erosion and topography of the watershed and was significant (P≤ 0.001). The amount of observed sediment had a positive correlation with slope, circulatory ratio, rainfall, topography, and area of the watershed and was significant (P≤ 0.001). In order to influence the factors and variables on the amount of sediment in the sub-watersheds, the method of principal component analysis and cluster analysis were used. The results showed that the land use explained 25.24% of the variance of all research variables. Finally, the three factors of land use, upland erosion, and geology, and two parameters of circulatory ratio and slope were explained at 86%. Of the variance of all research variables.

The aim of this study is to prioritize effective factors, landslide susceptibility zonation assessment using maximum entropy (MaxEnt) and dempster shafer models in Doab Samsami watershed of Chaharmahal and Bakhtiari province. For this purpose, 15 factor maps affecting landslide occurrence as independent variables and landslide distribution map as a dependent variable were prepared and weighted using frequency ratio index (FR) and landslide distribution map in the environment ArcGIS® 10.8 . In order to implementation and validation of models, landslide distribution data were randomly divided into two categories of training and test data in the proportion of 70 and 30%, respectively. Maximum Entropy (MAXENT) and Dempster Shaffer models are performed and landslide susceptibility zonation maps are prepared and each model is divided into five very low to very high. In order to evaluate the classification accuracy and validation of the models, the frequency ratio and seed cell area index (FR&SCAI) and the area under receiver characteristic curve (AUC-ROC) were used, respectively. According to the results of the maximum entropy model, annual precipitation factors, lithology, distance to road and drainage land use are important in landslide occurrence, respectively. According to landslide susceptibility zonation maps in both models, more than 50% of landslides occurred in high and very high susceptibility categories. Finally, the validation results of the models showed that the Demester shafer model with AUC-ROC index of 0.95 and classification accuracy with higher FR & SCAI index, greater efficiency and desirability for zoning, modeling and landslide prediction in the study area.

 Landform refers to any physical feature of the surface with a recognizable structure and shape. Landform elements and structural forms of the terrain surface could, directly and indirectly, drive many other environmental variables. Numerical representation of the surface and uneven pattern of the earth is a common topic in geographical, geomorphological, geological, and geophysical hazard mapping as well as sea-bed exploration. The combination of the earth and computer science with mathematics and geomorphometric engineering interacts with discrete and continuous landforms. Geomorphometry dates back to about 150 years ago and the work of Alexander von Humboldt and geomorphologists, and today with the revolution in computer science and especially digital computer models is developing rapidly. Detection and classification of landforms are of interest to GIS developers, geoscientists, and geomorphometry researchers. In this way, the desired work units are extracted with higher speed and accuracy and used in the form of vector and raster maps. Existing approaches are mainly based on height, terrain derivative, gradient, curvature, flow direction, slope position, morphometric indices, and the like. Also, less attention has been paid to the challenge of matching the diagnostic scale with the Landform scale, and most models have this shortcoming. On the other hand, less attention has been paid to the possibility of vectorization output results and also to the analysis of sensitivity and temporal response algorithms to machine processing. In this research, we attempted to recover and resolve the mentioned shortcoming and problems in the previous works. In this research, using basic algorithms of raster analysis and coding, new methods and algorithms for the automatic detection of landforms have been developed. Focal raster analysis is also emphasized and the moving window technique is used to implement the algorithms. Facing the scale challenge, sensitivity analysis, and the response algorithms to input changes as well as accuracy assessment are other aspects that have been addressed in this research.

 In this study, the Digital Surface Model (DSM) published by the Japan Space Agency in May and October 2015 with a horizontal resolution of about 30 meters was used to work on the topography of the region. These data are obtained from ALOS satellite images. This database is based on DSM data (5m network version) 3D topography, one of the most accurate elevation data on a global scale. The digital elevation model was transformed into a matrix structure using a Python coding environment. Then, raster analysis was implemented using the moving window technique. The moving window algorithm was coded in a way that the dimensions of the moving window could be freely determined and changed. In proportion to the size of the moving window, some adaptive algorithms are implemented to automatically correct and organize the edge effect in proportion to the size of the moving window. In this study, automatic landform detection was performed using spatial analysis of kernel patterns in the raster grid of digital elevation models and the results were presented in the form of three algorithms applied in the detection of topographic peaks and ridges. These algorithms include Multilevel Mean Summit Recognition Algorithm (MLMSR), Complex Multilevel Summit Recognition Algorithm (CMLSR), and Single Point Summit Recognition (SPSR). Each of these three algorithms was first conceptually designed and then coded and executed using the Python programming language. In the next step, the sources of error and specific scenarios of the algorithms were examined. The sensitivity of each algorithm related to the dimensions of the moving window, the resolution, and the size of the raster file, was evaluated, and finally, the accuracy and validation of the three models, using reference layers that were manually prepared and plotted, were assessed. All the procedures were designed in a way that could easily be implemented in an official software and were completely compatible with the structure of machinery processing. Also, being automatic and working on different platforms where one of our priorities.

 In the automatic detection of peaks and ridges using a digital terrain model, kernel spatial pattern analysis was used. In this regard, three proposed algorithms in this field were designed, coded, and executed. The output results of each of the algorithms were presented in the form of a raster and vector data model. Accuracy and sensitivity assessments were performed by considering changes in moving window size, resolution, and raster grid size (row x column) for each of the algorithms. The MLMSR algorithm tends to be in a more binary result in the lower dimensions of the moving window, while the CMLSR and SPSR algorithms do not. In all algorithms, increasing the size of the moving window causes a more generalization ratio. CMLSR and SPSR algorithms are more suitable for cartographic and visual purposes due to the higher degree of grading in the results. Regarding the temporal performance (Runtime) or sensitivity to input changes, the SPSR algorithm performs better. This is especially important when the input file size (number of rows and columns) is large. According to the results of validation and accuracy evaluation, MLMSR and SPSR had better performance than, the CMLSR algorithm. Python programming language has been widely used in the design and implementation of all algorithms, as well as in the field of sensitivity evaluation and validation. Totally more than 500 lines of codes were done for this purpose. All algorithms are automated and are able to execute and store results in raster and vector format using machine processing.

 The results show that the MLMSR algorithm in smaller dimensions of the moving window is tending to more binary results, which is problematic in some graphical and cartographic applications, but the CMLSR and SPSR algorithms showed more gradual trends in their outputs and so, they performed better in this respect. Researchers who intend to study and develop in this field are advised to focus on adaptive algorithms and optimize the dimensions of the moving window in relation to the volume of input information and so, in this way, they increase the flexibility of algorithms in relation to input changes.

Nowadays, landslides are among the geological hazards that data mining methods based on machine learning have been developed to model and predict. This paper addresses the development of a landslide susceptibility assessment that uses machine learning techniques and GIS. Artificial Neural Network (ANN) and Random Forest (RF) were compared for the landslide spatial modeling. The landslide susceptibility zoning maps consider 15 layers including geologic, morphologic, hydrologic, man-made parameters (independent variables) for landslide susceptibility assessment, and Doab Samsami watershed in Chaharmahal Bakhtiari province was chosen for the application of models due to data availability and the 174 total landslide occurrences (dependent variables). The relationship between effective factors and landslide occurrences was quantified and weighted using frequency ratio. Data independence was tested using linear multivariate regression analysis, tolerance, and VIF indices. In order to implement and validate the model, the landslide locations were randomly divided into two subsets, namely, training (70% of the total) and testing (30%), respectively. Subsequently, RF and ANN models were developed and the landslide susceptibility zonation map was produced. Maps were evaluated and validated using frequency ratio & seed cell area index, success rate, area under of receiver Operating characteristic (AUC-ROC). Results illustrated that the two factors of slope length and topographic wetness index have multicollinearity or information overlap and were removed from the modeling process in later stages. Effective factor analysis in both models showed that lithology, land use, and aspect slope factors had a significant effect on landslides, respectively. Also, the results of classification and validation of models showed that the random forest (RF) model (AUC-ROC = 0.919) was more accurate and efficient than the artificial neural network (AUC-ROC = 0.845) for landslide occurrence prediction. The results of this study can be used by executive administrations for management and planning in development and executive plans, including rural-urban development, accurate estimation in erosion models in watersheds, soil conservation, and natural resources protection.

Delineation of gully erosion susceptible areas by using statistical models, as well as optimum usage of existing data and information with the least time and cost and more precision, is important. The main objective of this study is to determine the areas accuracy to gully erosion and susceptibility mapping by using data mining of the bivariate Dempster-Shafer, linear multivariate statistical methods and their integration in Semirom watershed, southern Isfahan province. First, the geographical location of a total of 156 randomly gullies were mapped using preliminary reports, satellite imagery interpretation and field survey. In the next step, 14 conditioning parameters of the gullies in the study area were selected including the topographic, geomorphometric, environmental, and hydrologic parameters using the regional environmental characteristics and the multicollinearity test for modeling. Then, the Dempster-Shafer statistical, linear regression, and ensembled methods were developed using 70% of the identified gullies and 14 effective parameters as dependent and independent variables, respectively. The remaining 30% of the gully distribution dataset were used for validation. The results of the multivariate regression model showed that land use, slope and distance to drainage network parameters have the most significant relation to gully occurrence. The gully erosion susceptibility maps were prepared by individual and ensemble methods and they were divided to 5 classes of very low to very high rate. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve was used to validate gully erosion susceptibly maps. The verification results showed that the AUC of ensemble method (0.948) is higher than Dempster-Shafer (0.924) and Multivariate regression (0.864) methods. Also, the the seed core area index (SCAI) value of the ensembled model from very low to very high susceptible classes have a decreasing trend that indicating a proper separation of susceptible classes by this model.

Land subsidence as a hydrogeomorphology event is currently occurring dangerously in many plains of the country due to uncontrolled groundwater extraction from water bearing layers, and accordingly monitoring and studying this phenomenon seems to be necessary. In this study, land subsidence rate of the Najafabad aquifer was determined through the Differential Radar Interferometry (DInSAR) processing of the ASAR and PALSAR radar data and the results were validated by comparying with the differential leveling and groundwater level drowdown data. Processing of the ASAR sensor data estimates the land subsidence in the Najafabad plain at an average annual subsidence rate of 6.7 cm and a total of 41 cm during 6 years period and processing of the PALSAR data suggests an annual rate of 7.7 cm and total subsidence of 30 cm during 4 years period. Most of the occured displacements are related to the Tiranchi, Koushk, Ghahderijan, Goldasht and Falavarjan cities. The simultaneous groundwater level data with acquisition date of the radar satellite images between 2002 and 2014 shows a drawdown ranging from 0.5 to 46.5 meters in the south and east to north of the Najafabad city consistent with the estimated land subsidence areas.The DInSAR processing of the PALSAR data has led to a more accurate results with higher spatial resolution. Results of the radar data processing can be employed for the hazard zonation directly utilized for management and planning of control and preventive measures.

Selection of the appropriate distribution function and estimation of its parameters are two fundamental steps in the accurate estimation of flood magnitude. This study relied on the concept of optimization by meta heuristic algorithms to improve the results obtained from the conventional methods of parameter estimation, such as maximum likelihood (ML), moments (MOM) and probability weighted moments (PWM) methods. More specifically, this study aimed to improve flood frequency analysis using the Artificial Bee Colony algorithm (ABC). The overall performance of this algorithm was compared to the conventional methods by employing goodness of fit statistics, correlation coefficient (CC), coefficient of efficiency (CE) and root mean square error (RMSE). The study area, Babolrood catchment located in southern bank of Caspian Sea, has been subjected to annual flooding events. A total of 6 hydrometry stations in the study area were delineated and their data were used in the analysis of 6 distribution functions of Normal, Gumbel, Gamma, Pearson Type 3, General Extreme Value and General Logistic. This analysis indicated that Gamma and Pearson Type 3 were the most appropriate distribution functions for flood appraisal in the study area, according to the ABC and conventional methods, respectively. Also, the results showed that ABC outperformed ML, MOM and PWM; so, Gamma could be recommended as the most reliable distribution function for flood frequency analysis in the study area.

East of Isfahan City, especially around Segzi Plain is one of the desert areas of the country that due to the flatness and soils sensitivity to wind erosion, there is high susceptibility to wind erosion. The aim of this study is to evaluate the possibility of using saline water of Segzi Plain and its effect on the soil strength properties, crust formation and its stability against wind erosion. In order to conduct this research, five soil samples with different textures were collected from top soil surface and were transferred to the laboratory along obtained saline water sample from surface aquifer. After determining some of the physical and chemical properties of samples, they were examined in wind tunnel with specified velocity for soil erosion tests. The first soil sample was flooded with saline water and the rest were treated with either spraying of undiluted saline water or spraying of diluted saline water with 2 to 1, 1 to 1 and 1 to 2 ratios of saline water to water. In this regard, parameters such as salinity of saline water, erosion threshold velocity of dried treated soil samples, maximum dry density, thickness, strength and sieve analysis of the crusts were determined. The results indicated that, as the salinity of saline water increases, the strength, thickness and maximum dry density of forming crust and wind erosion threshold velocity also increases in the model. Analysis of variance used to investigate the effects of soil texture, salinity of saline water, crust thickness and threshold velocity to control wind erosion showed significant difference in 1% level. Sample C1 with the highest percentage of fine grains had threshold velocity of 11 m.s-1, but sampleE1 with the lowest percentage of fine grains had threshold velocity 6.23 m.s-1. The presence of a high amount of sodium makes restrictions on the possibility of using saline water as mulch scientifically and practically but the results showed that the use of saline water can increase the density of dirt roads.

Water resources management depends on the precise assessment of water storage and access in each region, as well as environmental interactions of these resources. The man objective of this study was to delineate the potential zones of groundwater storage using FAHP. Mapping and assessment of it required maps of geomorphology, drainage, density, lineament density, slope and vegetation, which were initially prepared as the input layers in FAHP; the appropriate weights were attributed to them based on FAHP. Potential zones of ground water were classified into five classes of poor, average, good, very good and excellent. The number and density of available wells and springs in the study area dealt with the potential of the region for groundwater storage. So, ROC was used to assess the validation of results, considering spring points as signs of water resources. According to the results, classes of very good, good, average, weak, and very weak were ranked as the first to the last in terms of privilege order with an area of 37.7, 55, 40, 107, and 98.4 square kilometers, respectively.

Identification of areas that prone to subsidence and estimation of its rate plays an important role in the control management of this phenomenon. Differential interferometry radar technique (D-InSAR) with very high accuracy is one of the most suitable ways for identify and measure the rate of subsidence. In this study, to identify and measure the subsidence in Mahyar Plain differential radar interferometry techniques have been used in the period of 2004 to 2010. For this purpose, eight pair images of time series were used from ASAR sensor in C-band radar in ascending passage. The method used in this study is based on laboratory-field surveys. For validation of technique, survey data such land use and geology maps and data of observation wells in the region were used. As a result, maximum rate of annual subsidence in the area was 6.4 cm yr-1. Also, results showed that the highest amount of subsidence occurred in areas under cultivation and due to excess extraction of groundwater and subsidence of aquifer surface. The rate of subsidence was obtained 0.384 cm for each two cm drop of water table according to the relationship between subsidence and the changes of piezometric wells surface.

Today, supplying water to meet the sustainable development goals is one of the most important concerns and challenges in most countries. Therefore, identification of the areas with groundwater potential is an important tool for conservation, management and exploitation of water resources. The purpose of this research was to prepare the potential groundwater map in Nahavand, Hamedan Province, using the weight of evidence model and combining it with logistic regression. For this purpose,  the information layers of slope angle, slope aspect, slope length, altitude, plan curvature, profile curvature, TWI, SPI, distance from fault, fault density, distance from river, drainage density, lithology and land use were identified as the  factors affecting groundwater potential and digitized in the ArcGIS software. After designing the groundwater potential map with these three methods, ROCs were used to evaluate the results. Of 273 springs identified in this study, 191 (70%) were used to prepare the groundwater potential map and 82 springs (30%) were used to evaluate the model. The area under curve (AUC) obtained from the ROC curve showed an accuracy of 80.4% for the weight of evidence model and 82.5% for the weight of the evidence- regression combined model

The first and most important step in reducing damages and management of gully erosion, which leads to waste of water and soil resources, is the gully erosion susceptibility mapping. In present study, the potential of gully erosion in the Toroud-Najarabad basin were evaluated using the uncertainty of the theory of evidence method and linear multivariate regression as well as the combination of the above-mentioned methods. In view of this the gully erosion inventory map was prepared using various resources, and from 218 identified gullies, 70% (154 gullies) and 30% (64 gullies) were selected randomly for modeling and validation respectively. In the next step, 14 controling factors in the gully occurrence in the study area including topographic, geomorphometric, environmental and hydrologic parameters were determined for modeling. The results of multivariate regression model showed that land use parameters, slope and distance from the road had the greatest impact on gully occurrence. Susceptibility maps classified to five susceptibility classes of very low to very high. The area under curve (AUC) and Seed Cell Area Index (SCAI) were used to validate the susceptibility maps. The results of the validation showed that the combined model has a higher area under the curve (AUC) (0.933) toward the evidence-based (0.914) and multivariate regression (0.890) models, as well as, the SCAI value in the combined model showed that very low susceptibility to a very high susceptibility has a downward trend that indicating a proper separation of susceptibility classes in this model. Considering the excellent precision of the combined method proposed in this research, the results of this research can be used by decision makers and local managers to reduce losses and the methodology presented in this study can be used in similar areas to determine the areas that are susceptible to gully erosion.

Landslide is a natural disaster resulted from geomorphologic, hydrologic, and geologic conditions along with human factors. As of present, many pieces of research have been performed to achieve such a purpose based on various models. However, many of such works have failed to provide satisfactory results due to failure to consider surface landform, so that limitations have been encountered when it came to the application of their findings. This highlights the necessity of using novel methods based on quantitative criteria of landform to identify the zones susceptible to landslide, so as to conduct proper planning for such zones. In this respect, morphometric parameters can represent landform characteristics of the hillsides susceptible to landslide. Moreover, in tectonically active areas, instability of the hillsides can be observed in the form of various types of landslide. This shows that, when it comes to the assessment of landslides, one should further consider neotectonic activities by means of geomorphologic indices.
In the present research, a total of 20 effective factors were used to assess accuracy of a landslide susceptibility zoning map; the factors include 14 conventional factors along with 6 geomorphometric factors. For this purpose, firstly, independence of the factors affecting the landslide was analyzed by performing Multicollinearity reach tests. To this end, two important indices, namely tolerance (TOL) and variance inflation factor (VIF), were used to undertake multi-directional reach test. These two indices are commonly used when running generalized linear models for analyzing the relationship between independent variables or conducting multi-directional reach test. Even though no consistent principle is presented for determining thresholds of these two indices (VIF and TOL) in multi-directional analysis and estimation of the factors affecting landslide, but records of the research works performed on this topic show that, multiple-correlation problem will arise (i.e. the data or independent variables are not correlated at all) if the value of VIF is smaller than 5 or 10 and the value of TOL is higher than 0.1 or 0.2. Moreover, in order to assess the correlation between the landslide and the selected factors, the weights obtained from the confidence factor (CF) model were used in the form of bivariate statistical analyses. The weights calculated by this model were further employed to prepare factorial maps and convert them into binary maps (wherein the levels with negative and positive weights were represented by 0 and 1, respectively) to be introduced into the conditional independence test. Continuing with the research, once finished with entering the weights obtained from the CF to the logistic regression model, the model coefficients were extracted. Using the obtained weights, the model was run to prepare a landslide susceptibility zoning map following either of two approaches, i.e. with and without considering geomorphologic indices along with other effective factors. Finally, Receiver Operating Characteristic (ROC) curve was used to validate, assess, and compare the maps obtained via either of the two approaches.
Based on the results of multiple correlation test, no correlation was obtained between the independent factors with VIF value of at most 3.559 and TOL values of at least 0.253. All of the values of VIF of the independent factors were lower than the critical value (5 or 10), while all of the values of TOL of the independent factors were higher than the calculated value by the critical theory. Maximum and minimum values of VIF were found to be 3.559 and 1.101, respectively, and the corresponding values to TOL were 0.253 and 0.895, respectively, which referred to the land roughness and slope direction, respectively.
The results obtained from the CF model showed that, the land roughness values exceeding 14 followed by slopes exceeding 40% possess the largest CF weights, among other levels of the considered factors, while waterway densities ranging between 0 and 10 and precipitations lower than 550 mm exhibited the lowest CF weights. According to the results obtained from the logistic regression model, the slope, NDVI, and slope direction exhibited the highest correlation coefficients, making them the best predictors of landslide occurrence in the region. Combing the weighted maps, zoning maps were prepared via the two approaches. Accordingly, 17.06% and 8.27% of the area understudy were identified as very highly susceptible to landslide, for the cases with and without considering the effects of geomorphologic factors, respectively. In addition, based on the results of ROC, the area under the curve was evaluated as 0.88 and 0.82 for the cases with and without considering the effects of geomorphologic factors, respectively. This confirms higher efficiency of the models into which geomorphologic factors are incorporated along with other common parameters considered in landslide susceptibility zoning.

Catchment prioritization in terms of natural disaster intensity as well as prevention and control practices plays a main role in the natural resources and watershed management. In this study, a total of 24 sub-catchments in the Zohreh-Jarrahi basin were prioritized according to their morphometric parameters and using the mixed model of TOPSIS-Multivariate regression. A total of 12 morphometric parameters including constant component of channel maintenance, drainage density, ruggedness number, infiltration index, stream power index, stream frequency, slope, drainage texture rate, relief rate, form factor, bifurcation ratio and topography wetness index, in addition to rainfall, were studied and scored. Parameters were weighted by using multivariate regression and the spatial distribution of the observed flood events. TOPSIS model was used for the prioritization process. The results obtained from the weighting analysis showed that the ruggedness number, slope and rainfall had the highest effect on flooding in the study area with the score of 0.068, 0.024, and 0.016, respectively. According to the prioritization results, sub-catchments of Seidoon, Emamzadeh Jafar, and Takht Deraz, which had the minimum distance to the positive optimum (0.0028, 0.0029, and 0.0029, respectively) and the maximum distance to the negative optimum (0.0097, 0.0098 and 0.0095, respectively), showed the highest flooding intensity with the score of 0.7745, 0.7690 and 0.7625, respectively. In order to validate the results, prioritization results were compared to the observed flood events. Validation results also indicated the efficiency of the mixed model in delineation of catchments prone to flooding. Three sub-catchments of Seidoon, Emamzadeh Jafar and Takht Deraz were observed to have the highest number of observed flood events, thereby showing the high effectiveness of the model and also, the role of the morphometric parameters in flooding.

Distinguishing the susceptible areas to landslide using appropriate experimental models of landslide susceptibility mapping is one of the primitive and basic works to reduce probable damages and reduce risk in country's watersheds. In this research, thirteen factors, including altitude, slope, aspect, rainfall, plan curvature, profile curvature, Topographic Wetness Index (TWI), Stream power index (SPI), distance from the river, distance from road, distance from the fault, lithology, and land use are identified as effective parameters in landslide occurrence in the studied area. The landslide landslide distribution map was prepared using Landsat 2017 satellite imagery and Field studies (2017). Then, the landslide susceptibility mapping was performed with three methods, including frequency ratio, entropy and TOPSIS. The results were evaluated using receiver operating characteristic (ROC) curve. The area under the curve (AUC) of applied models shows the accuracy of 84, 87.7, and 91 percent for entropy frequency ratio and TOPSIS models, respectively. The results are indicative statistical models have a better accuracy in comparison with MCDM models and expert approaches. The results of the present study could be useful for better managing the flood affected areas and to reduce its damage.

One of the goals of geomorphologists in working with the models of different landforms is to obtain better relations in realizing the physical realities of environment. In this study, to evaluate the performance of geomorphometric parameters to increase accuracy of zoning landslide susceptibility map has been studied. As the first step by the application of nine initial conditioning factors including slope, aspect, elevation, land use, lithology, distance from roads, rivers and vegetation index (NDVI) the zoning map was provided. In the next step geomorphometric parameters influential on the occurrence of landslide including topographic location index (TPI), surface curvature, curved sections, slope length (LS), Topographic wetness index (TWI), stream flow power (SPI), surface area ration index (SAR), was added to the model and then the zoning map was obtained. In the final step, the zoning maps was evaluated by using ROC curve. To provide zoning maps a new mixed model was applied, so, for determination of criteria weights multivariate regression and to determine weight of the classes' frequency ratio method was utilized. The findings of this research indicated that geomorphometric factors have a considerable influence on the increase of identification of regions that are susceptible to the landslides and enhance the accuracy of zoning maps from 0.731 to 0.938. These factors have also increased the resolution of the slip classes. According to the results, topography position index, plan curvature and surface area ratio have the highest influence on the accuracy of zoning maps. Based on superior approach, 8.68% (6737 ha) of the region are at very high risk and 15.3% (11906 ha) have been identified as high risk areas. According to the high ability of geomorphologic parameters in the identification of susceptible areas to the landslide, the application of these parameters is recommended in landslide hazard zonation.