https://hyd.tabrizu.ac.ir/ Journal of Hydrogeomorphology en daily 1 Sat, 21 Mar 2026 00:00:00 +0330 Sat, 21 Mar 2026 00:00:00 +0330 https://hyd.tabrizu.ac.ir/article_19754.html One of the most important forms of geomorphological processes on the Earth's surface is landslides. Landslides are dynamic processes that play a role in the change and evolution of slopes (Guzzetti et al., 2005:274). This phenomenon occurs due to the displacement of constituent materials on slopes; the result is the movement of a large mass of materials down the slopes (Maddi, 2009:77). The displacement of soil and rock masses on slopes originates from two external and internal factors.However, this phenomenon causes the formation and evolution of slopes in mountainous areas (Roering et al., 2005:655). But they cause damage or death to humans and also significant financial losses to mountainous areas, agricultural lands and facilities in that area such as communication roads and development sectors (Mohammadnia et al., 2018:115). In this context: (Maddadi et al., 2019), conducted a comparative evaluation of MABAC and CODAS multi-criteria decision-making in landslide risk zoning in Kausar County. (Yalcin, 2008), evaluated landslide areas in the Ardsin region of Turkey using the analytic hierarchy process and statistical index and weighting factor. Therefore, considering that no research has been conducted on landslides in the Onarchay region so far. Therefore, the purpose of this study is to investigate the various factors affecting the occurrence of landslides in the Onar Chay watershed of Meshkinshahr and to zoning the landslide risk using the (MABAC) model. https://hyd.tabrizu.ac.ir/article_20377.html The research steps were as follows: after preparing piezometric well statistics, the data highlighting method was used to eliminate the deficiencies in the study data. The highlighting method used was the interpolation method, which was performed by Neural Power software (based on artificial neural networks), to eliminate the deficiencies in the data. Logarithmic transformation was used in SPSS software to normalize the data, and GS+ software was used for geostatistical analyses. ENVI5.3 software and radiance and flash methods were used for atmospheric, radiometric, and geometric corrections, and GIS10.5 software was used to extract the desired maps. Object-oriented classification method was used in eCognition Developer64 software to classify land use. In the object-oriented classification method, spectral information is combined with spatial information and pixels are segmented based on shape, texture and gray tone in the image surface with a specific scale and image classification is performed based on these segments (Faizizadeh & Hilali, 2010: 77). In segmentation, pixels are segmented by different algorithms in different sizes, with different spectral and shape ratios and are classified into various objects based on spectral and spatial characteristics. During this process, image objects are created according to their homogeneity or heterogeneity based on scale, color, shape, smoothness coefficient and compression shape parameters. https://hyd.tabrizu.ac.ir/article_17160.html Changes in land cover and land use due to human activities have left adverse effects on the environment. The eastern regions of Ardabil province are a clear example of this phenomenon. The purpose of this research is to analyze spatial and temporal changes in land cover and land use and its effects on the temperature of the surface of the earth in Lake Neor. To estimate land use and land cover, random forest models (RTC), maximum likelihood model (MLC) and support vector machine (SVM) were used and the efficiency of each was estimated by the Kappa coefficient and it was observed that the SVM model has the highest Kappa coefficient (0.87) Bands 6, 5 and 10 of Landsat 8 were also used to extract the LST index, and it was observed that the western part of the lake faced an increase in the temperature of the earth's surface. During the time period of 2002, 2013 and 2022, significant changes were observed in the water area of Neor Lake and its nearby vegetation. Barren lands had the largest extent in all studied periods. Vegetation has increased by 1.04 square kilometers based on SVM model. The surface area of the lake was estimated as 3.19 square kilometers based on the MLC model in 2002. The area of the water zone in the MLC model has decreased by 1.56 square kilometers between 2002 and 2022, and this decrease is 0.67 and 0.69 square kilometers for the RTC and SVM models, respectively. https://hyd.tabrizu.ac.ir/article_20461.html Flooding is one of the natural disasters that causes economic losses and the death of many people every year. Among natural disasters, flooding is the deadliest crisis. The vastness of Iran, along with the diversity of climate and the spatial and temporal changes in rainfall in its watersheds, cause massive floods in the country every year. In recent years, the increase in rainfall due to the impact of climate change has been the main cause of flood risks.Therefore, the aim of the present study is to zone the flood risk sensitivity of the Khoy County basin. For this purpose, first, using Landsat images and object-oriented classification techniques, they were extracted and classified into classes (agricultural and garden lands, saline lands, residential lands, pastures and barren lands, and rocky outcrops). In the next stage, by identifying the factors affecting the flooding of the region and preparing information layers for each criterion in GIS, the standardization of the layers was carried out using the fuzzy membership function, the ranking and weighting of the criteria was carried out using the critical method and the ANP method using the Super Decision software, and the final modeling was carried out using the ANP multi-criteria analysis method. Then, the sensitivity analysis of the criteria was performed using the training data. Then, by applying different stages of the model on the maps, the flood sensitivity zoning map of the basin of the region was extracted in 5 classes from very high risk to very low risk. https://hyd.tabrizu.ac.ir/article_18153.html Spatial and temporal changes of surface water affect the structure and functioning of the ecosystems of the Teham Dam region as well as the agricultural, economic and social development in this region. In this research, MNDWI, AWEI and NDWI indices and SVM support vector machine model was used to detect the long-term changes of Teham Dam in the period from 2002 to 2023. The results of the AWEI index showed that the area of the dam was about 2.4 square kilometers in 2007, which decreased to 1.15 square kilometers in 2023. In the MNDWI index, in 2007 and 2023, the area of water was equal to 2.6 and 1.17 square kilometers, respectively. The NDWI map shows a 46.38% decrease in the area of the water zone from 2007 to 2023. But in the AWEI index, this decrease was equal to 47.9. AWEI index with kappa values equal to 0.94 has correctly recognized the boundaries of water areas. According to the SVM model, in this period of time, the amount of vegetation has decreased from 0.8 square kilometers in 2002 to 0.07 square kilometers in 2023. The amount of barren land has decreased almost in this period of time and was equal to 4.57 square kilometers in 2023. The maximum temperature of the earth's surface in July 2002 was equal to 38.3 degrees Celsius and in July 2023 it reached 28.4 degrees Celsius. https://hyd.tabrizu.ac.ir/article_20485.html This study aims to analyze land subsidence variations and examine their relationship with groundwater level decline in the Urmia Plain aquifer, employing the Differential Interferometric Synthetic Aperture Radar (DInSAR) technique and Sentinel‑1 satellite data for the period 2015–2023, processed in the SNAP software environment. The results indicate that the highest subsidence rate, approximately 9 cm/year, occurred in the southern parts of the study area, whereas in the northern parts, ground uplift (positive displacement) was observed.Concurrently, hydrogeological data from 23 piezometric wells over the period 2002–2022 reveal a severe groundwater level decline of more than 13 m during the past two decades. A key finding of the research is the presence of a nonlinear relationship between groundwater drawdown and subsidence intensity. Contrary to expectations, the largest extent of high‑class subsidence occurred in areas with moderate groundwater decline (−1.5 to −3.5 m), and the very high‑class subsidence zones were found in areas with low groundwater decline (0 to −1.5 m).The present study demonstrates that the intensity of land subsidence is not necessarily directly correlated with the rate of excessive groundwater extraction. Surprisingly, the central and southern regions, characterized by moderate water loss, experienced the most significant subsidence, whereas the northern areas, despite severe water decline, exhibited negligible subsidence. This heterogeneous pattern suggests that local geological characteristics likely play a more determinative role in the spatial distribution of subsidence. https://hyd.tabrizu.ac.ir/article_18776.html Soil erosion is one of the significant environmental challenges that leads to the degradation and decline of soil quality. This study examines the impact of land use changes on soil erosion in the Givi Chai region. The use of satellite images (ETM+7 2010 and OLI 2022) as the primary tool for analyzing land use changes and assessing soil erosion has been a crucial step in this research. The images were classified using an object-based method, identifying various land uses. These uses included residential areas, agricultural lands, barren lands, orchards, dense, medium, and sparse vegetation, and water bodies. The factors influencing the G2 model included R (rainfall), S (soil erodibility) , V (vegetation cover), T (topography) , and I (slope adjustment factor). An erosion map was created and analyzed. Results indicated an increase in residential areas and sparse vegetation while showing a decrease in orchards and medium vegetation cover. These changes directly impacted soil erosion. The amount of erosion in 2022 increased compared to 2010, with the highest erosion occurring in agricultural and barren land uses. The final erosion map indicated areas with very high and high erosion associated with agricultural land use, weak pastures, and residential areas. The increase in agricultural and barren lands and the decrease in vegetation cover were identified as the main factors contributing to soil erosion. The study shows that changes in land use, particularly the increase in residential and agricultural areas, have a negative impact on soil erosion. https://hyd.tabrizu.ac.ir/article_20447.html With the increasing complexity and dynamics of hydrological systems, accurate and reliable river stream flow prediction is necessary for sustainable water resource management. This research utilized 20 years (from 2001 to 2021) of daily precipitation, river discharge, and mean air temperature data from the Dehgolan basin in Kurdistan Province. To select the optimal combination and model scenarios, Pearson's correlation coefficient was employed using precipitation (Pt), mean temperature (Tt), and river discharge with one to three days of lag (Qt-1 to Qt-3). The Pearson correlation coefficient (PCC) was used to select optimal scenarios and model combinations, establish the relationship between input and output variables, and subsequently choose the model and scenario combinations. For streamflow prediction, we utilized hybrid models including the Artificial Neural Network-Tasmanian Devil Optimizer (ANN-TDO), Support Vector Regression-Red Tailed Hawk (SVR-RTH), and the deep learning model Long Short-Term Memory-Marine Predators Algorithm (LSTM-MPA). Model evaluation involved the following metrics: Root Mean Square Error (RMSE), Coefficient of Determination (R2), and Kling-Gupta Efficiency (KGE). Among the hybrid models, ANN TDO consistently demonstrated the best performance. Its R2 values for the testing phase were frequently above 0. 8, and KGE values reached up to 0. 915 in some scenarios, indicating a very high correlation with observed data. The LSTM-MPA model also delivered very good performance. Although it performed slightly below ANN-TDO in some scenarios, its R2 and KGE values (often above 0. 7 and 0. 8), along with low MAE and RMSE values, demonstrated this model's high capability in time series modeling. https://hyd.tabrizu.ac.ir/article_20451.html One of the methods for estimating floods in these basins is to use hydrological modeling.The purpose of this study is to analyze the maximum flood discharge using fractal dimensionandWinTR-55, NRCS, and HEC-HMS models in some sub-basins of the Malayer Dam inHamedan usinggeomorphological data.The study shows that in the Acsub-basin there is an acceptable agreement between the NRCS unithydrographs and the fractal dimensionunit, and the HEC-HMS model output also indicates that there is anacceptable agreement between the observational data and the modeloutput in the Ac sub-basin, which is observed in the fractal dimension and the HEC-HMS output as a single NRCS hydrograph. The results of calculating peak discharge using theGeomorphological Unit Hydrograph method, despite the small size and small changes in the basin's geomorphological parameters, show that the peak discharge values estimated in the GIUH method differ little from the observed and calculated discharges using the WinTR-55 model, which means that geomorphological parameters play an important and effective role in estimating peak discharge. Studies have shown that the hydrographs of the WinTR-55 model provide acceptable measurements of observational data in return periods of less than 5 years, and as the return periodincreases, the accuracy of the model decreases. In addition, the model is more sensitive to the values of the CNcurve number, indicating that the model provides acceptable results for sub-basins and isnot suitable for larger basins. Therefore, it is suggested that for small basins, the winTR-55 model and the fractal dimension with higher and more accurate drainage density be used. https://hyd.tabrizu.ac.ir/article_20795.html Locating watershed constructions traditionally and based on field visits requires a lot of money and time. In this study, the Analytical Hierarchy Process (AHP) approach was used to identify suitable locations for implementing watershed constructions. Three watersheds of Aqevlar Talesh, Masouleh Fouman, and Totkabon Rudbar in Gilan Province were selected for the study. Then, using expert opinions, 21 criteria affecting the location of watershed constructions were identified in 8 general categories and compared binary, and final maps were prepared with three classes of no potential, medium potential, and high potential for the construction of structures. The results showed that the criteria of discharge, precipitation, runoff height, and slope are of great importance in locating suitable areas for constructing structures. The accuracy of this method was determined using the receiver operating characteristic (ROC) curve and the area under the curve (AUC) for the Aqevlar, Masouleh and Totkabon basins was 0.945, 0.958 and 0.788, respectively. Comparing the location of the structures with the research results showed that 90 and 99.5 percent of the Masonry wall and gabion structures were located in the medium and high potential classes of the maps obtained from this method, respectively, which confirms the accuracy of this method in determining the appropriate location for the construction of watershed constructions. Therefore, it is recommended that the Natural Resources and Watershed Management Organization of the country use this model systematically for locating watershed constructions. https://hyd.tabrizu.ac.ir/article_20755.html Firstly, it compares and selects the most efficient hybrid artificial intelligence model (Wavelet Support Vector Regression, Whale Optimization Algorithm-Support Vector Regression, and Particle Swarm Optimization-Support Vector Regression) for accurate discharge estimation based on historical data from 1992-2022. In the next step, climate projections from General Circulation Models (GCMs) under various greenhouse gas emission scenarios are used to predict the future trend of river discharge from 2023-2043. Additionally, statistical indices such as the correlation coefficient, root mean square error, mean absolute error, and Nash-Sutcliffe efficiency coefficient were used to compare the performance of the hybrid models investigated. The results from evaluating the hybrid models showed that the Wavelet-Support Vector Regression model demonstrated better performance compared to other models studied, with the highest correlation coefficient of 0.980, the lowest root mean square error of 0.372, the lowest mean absolute error of 0.174, and the highest Nash-Sutcliffe efficiency coefficient of 0.985. Furthermore, the results from the evaluation of the LARS-WG model indicated that the CanESM5.0 model accurately predicts maximum and minimum temperatures but has errors in precipitation estimation, while the BCC-CSM2-MR model estimates higher precipitation during warmer seasons. Projections for the period 2023 to 2043 under different greenhouse gas emission scenarios, SSP126 and SSP585, suggest an increase in temperature, particularly in the high emission scenario SSP585, and uncertain fluctuations in precipitation amounts, with significant differences in these fluctuations among different models. Overall, the results of the flow prediction in the coming years indicated a significant decrease in the river’s discharge in the future. https://hyd.tabrizu.ac.ir/article_20370.html In mountainous regions, mass movement and landslide are regarded as a significant erosional process. Steep slope areas are geologically and ecologically sensitive and fragile, and the occurrence of landslides makes this area more vulnerable to many hazards. The primary goal of this research is to create a landslide susceptibility map using bivariate statistical models (frequency ratio and information value) and a multivariate model (logistic regression), and selecting the appropriate model for this task in Chalus County. The first step in this research is to prepare distribution map of the landslides and determine their location on the map. This was done using Google Earth imagery and field surveys. The maps of condiioning factors were prepared from different sources and entered into the GIS environment. frequency ratio, information value, and logistic regression as used to create landslide susceptibility map. The ROC curve and the area under the curve were used to assess the accuracy of the models. The AUC for frequency ratio, information value, and logistic regression were 0.705, 0.721, and 0.811. The area of the susceptibility classes were calculated for susceptibility map created using regression logistic. Results show that approximately 44 % of the study area is at high and very high susceptibility to landslides. The calculated area indicates that the region is highly susceptible to landslides, and paying attention to this phenomenon is very necessary. https://hyd.tabrizu.ac.ir/article_20636.html The Earth's surface temperature is of great importance as one of the key factors in controlling the biological, chemical and physical processes of the Earth. The Earth's surface temperature not only has a direct impact on natural processes such as evaporation, gas exchange and the water cycle, but also plays a fundamental role in studying climate change and weather patterns. In this study, the relationship between the Earth's surface temperature and vegetation cover and soil surface moisture in the land use of Kermanshah County was investigated. For this purpose, Landsat 8 satellite images with OLI and TIRS sensors were used in the years 2014, 2019 and 2024.After performing the image processing steps, the land use map was extracted using the supervised classification method in the Google Earth Engine environment using the SVM method in seven classes for a ten-year period. Also, the land surface temperature was obtained with a discrete window algorithm and its relationship with vegetation cover and surface moisture was investigated using statistical methods. The classification accuracy of the SVM method, by examining ground data and satellite images from 2014, 2019, and 2024, was 0.96, 0.97, and 0.97, respectively, in terms of the Kappa statistical coefficient, and 0.972, 0.970, and 0.977, respectively, based on the overall accuracy.separately and bands 3, 4, and 5 from the OLI sensor of the Landsat 8 satellite are used as combined layers. First, by converting the digital numerical values to the spectral radiance of the atmosphere or TOA, and then TB is calculated. https://hyd.tabrizu.ac.ir/article_20864.html In this study, with the analytic hierarchy process model, the information layers of lithology, distance from fault, slope, slope direction, elevation, distance from watercourse, distance from fault and land use were considered as factor maps. Also, in order to extract the karst water resources exploration model, shapefiles were retrieved. Different information layers were classified into criterion maps by applying expert opinions and field observations. Finally, according to the weights obtained in both AHP and ANP models, a karst water resources exploration map in South Khorasan Province was obtained. The results showed that in the AHP model, 42% of the total area of ​​South Khorasan Province was in the very underdeveloped category, 32% in the underdeveloped category, 17% in the medium category, 7% in the high development category and 2% in the very high development category. In the ANP model, 42% of the total area of ​​South Khorasan Province is in the very underdeveloped category, 33% in the underdeveloped category, 17% in the medium category, 6.5% in the highly developed category, and 1.4% in the very highly developed category. Therefore, the results of the study show that by comparing the efficiency of the two methods using validation with the well and spring layer, the ANP model has more validity, because after overlapping the two layers, in the ANP model, more wells and springs were placed on the developed and highly developed karst areas within the aquifer layer. https://hyd.tabrizu.ac.ir/article_20925.html Morphometric parameters, while describing the physical characteristics of a watershed, also control quantitative flood parameters such as discharge, time of occurrence, time of concentration, lag time, and flood hydrograph. For this reason, morphometric analysis is considered a low-cost, fast, and reliable method for flood assessment. This study aims to evaluate the flood susceptibility potential of various sub-watersheds in the Tanguieh basin using morphometric parameters and statistical models. After delineating the sub-watersheds, fifteen morphometric parameters were calculated for each one, and the relationships between these parameters and their influence weights were determined using the Pearson correlation test and Weighted Sum Analysis (WSA). Subsequently, the Sub-watershed Prioritization Index (SWPI) was calculated using the Weighted Linear Combination (WLC) method to prioritize the sub-watersheds for watershed management and flood control operations. Finally, analysis of variance (ANOVA) and statistical mean comparison tests, including Duncan’s test, were employed for grouping the sub-watersheds. The results indicated that based on the SWPI index, sub-watershed No. 1 (724.31) ranked first, followed by No. 2 (617.8) and No. 3 (570.196) in flood control priority. Additionally, according to the coefficient of variation (CV), sub-watershed No. 1, with an average score of 2.13, exhibited the highest flood susceptibility potential, followed by sub-watershed No. 3 (2.07) and sub-watershed No. 2 (1.8). However, Duncan’s mean comparison test revealed that although morphometric evaluations appeared to differentiate the sub-watersheds, there was no statistically significant difference among the three, placing them all in the same group. https://hyd.tabrizu.ac.ir/article_20938.html Land assessment for the purpose of identifying and zoning areas sensitive to slope movements is a research related to geomorphologists. The area of this basin is 8226 hectares. The studied area is located between the geographical coordinates of 07, 20, 46° to 30, 46° east longitudes and 17, 28, 38° to 52, 33, 38° north latitudes. The main objective of this study is to identify the factors affecting the occurrence of landslides and to zone landslide-prone areas. Therefore, to assess and identify high-risk areas, 9 factors affecting the occurrence of landslides were used, including distance from the watercourse, lithology, soil, slope, slope direction, elevation classes, precipitation, land use, and distance from the communication road. Multi-criteria decision-making algorithms were used to assess and zone the risk of landslides. The results show that the slope factor has the highest weight. Examination of the zoning map using the MACBETH model shows that 4.58, 12.25, 25.01, 39.34 and 18.78 percent of the area of the study area are located in the very high, high, medium, low and very low risk zones, respectively. The results of the performance evaluation of the MACBETH model indicate an acceptable accuracy of this model in predicting landslide zoning. The area under the curve (AUC) value for the training data is 0.86 and 0.88 for the validation data, which indicates the appropriate performance of the model in both data sets and its high ability to distinguish landslide-prone areas from non-prone areas. https://hyd.tabrizu.ac.ir/article_21080.html Monitoring morphological changes in meandering rivers, particularly in inaccessible areas or those lacking field data, has always faced operational challenges. Although remote sensing technology offers a promising solution to these challenges, most previous studies have focused on wide rivers, and its effectiveness for rivers with an average width of less than 50 meters has not been evaluated. This research aims to evaluate the accuracy of remote sensing methods in estimating migration rates and extracting river centerlines, using the Atrak River as a case study. In this study, using Sentinel-2 satellite images (from 2016 to 2021), six spectral indices (NDVI, NDWI, MNDWI, AWEI, EWI, WIR) were compared to extract the water surface of a section of the Atrak River near Korand and Hootan villages. After image preprocessing, the river centerline was extracted in QGIS, and its accuracy was evaluated using RMSE, length difference percentage, and spatial agreement with Google Earth reference data. Calibration results for 2016 showed that the MNDWI index, with the lowest error (RMSE = 6.09 m), the smallest length difference (0.47%), and the highest spatial agreement (94.13%), was the most accurate index for centerline extraction. Verification of this index for 2021 also confirmed its acceptable performance, with a length difference of 1.45% and an agreement of 85.95%. The findings clearly demonstrate the effectiveness of remote sensing and the superiority of the MNDWI index for quantitative and accurate monitoring of morphological changes in medium-width rivers. https://hyd.tabrizu.ac.ir/article_21265.html .Climate change is a significant threat to the global ecosystem, affecting both human life and the natural environment. While previously considered a natural seasonal change, human activities have accelerated the rate and severity of climate change. Floods are the most common natural hazard worldwide, accounting for almost half of all weather-related disasters. Land use maps in the years 1994, 2004, 2022 saw the decrease of garden lands and the increase of construction and the destruction of a large amount of vegetation in the region. The average amount of rainfall is the highest with 968 mm for the year1994, 835 mm for the year 2004 and 771 mm for the year2022. Based on the results, the amount of water retention in the basin for the years1994, 2004, 2022 is equal to 0.48%, 0.37% and 0.31%. The amount of water absorbed in the basin is equal to 1332, 12/01 and 10/56 in terms of million cubic meters in the mentioned years. The volume of flood realized for the years 1994, 2004, 2022 is equal to 21/56, 31.96, 42/94 million cubic meters, respectively. The amount of damages caused by flood for the years 1994, 2004, 2022 is equal to 7237 thousand dollars, 317908 thousand dollars and 884706 thousand dollars, respectively. https://hyd.tabrizu.ac.ir/article_21270.html Soil erosion is a major environmental hazard in regions characterized by complex topography and intense rainfall, posing serious threats to land sustainability, agricultural productivity, and hydrological systems. Among empirical erosion models, the Revised Universal Soil Loss Equation (RUSLE) is widely used due to its reliability and compatibility with GIS-based spatial analysis. A critical parameter in RUSLE is the topographic factor (LS), which is highly sensitive to the spatial resolution of the Digital Elevation Model (DEM). Inappropriate selection of DEM cell size can introduce substantial uncertainty into erosion estimates. This study aims to determine the optimal DEM resolution for accurate LS factor estimation in hazard-prone areas by integrating geostatistical techniques with GIS modeling. DEMs with spatial resolutions of 30, 50, 100, and 300 m were generated from topographic contour data and evaluated using semivariogram analysis and kriging interpolation. Geostatistical parameters including nugget, sill, range, and prediction error (RMSE) were systematically compared. The results indicate that a 50 m DEM provides the most balanced performance by preserving essential topographic variability while minimizing spatial noise and prediction error. The findings emphasize that DEM resolution should be selected based on statistical and spatial dependency analysis rather than arbitrary criteria. The proposed framework enhances the reliability of soil erosion assessment and provides valuable guidance for watershed management and hazard mitigation in erosion-prone landscapes. https://hyd.tabrizu.ac.ir/article_21276.html The data used included informational layers effective in land subsidence, such as: groundwater level decline (based on ten-year piezometric data), aquifer thickness and environment (based on drilling logs), recharge rate (using the Piscopo method integrating precipitation, slope, and permeability), pumping rate (based on discharge from exploitation wells), distance from faults, elevation and slope (from ALOS-PALSAR DEM), soil moisture (NDMI index from Sentinel-2 imagery), and land use. After preparing and rasterizing the layers in the GIS environment, the fuzzification process was performed using linear increasing membership functions (for factors like water level decline and aquifer thickness) and decreasing functions (for factors like recharge and elevation). Finally, the fuzzy layers were combined using the fuzzy gamma operator (with γ values between 0.7 and 0.9) to produce the final land subsidence potential map in five hazard classes (very low, low, moderate, high, and very high). Model validation was performed using subsidence data measured by three-frequency GPS. https://hyd.tabrizu.ac.ir/article_21343.html One of the hazards that has occurred in many plains of Iran in recent years is the hazards caused by subsidence. Identifying areas exposed to subsidence and estimating its rate plays an important role in managing and controlling this phenomenon. In this study, the high-precision radar interferometry technique is one of the most appropriate methods for identifying and measuring the amount of subsidence. This technique compares the phase taken from two radar data sets at two different times and, by creating an interferogram, is able to measure changes in the land surface over time. In this study, Sentinel 1 radar images from 2016 and 2023 were used to identify and measure subsidence in the Shahriar Plain, and SARSCAPE software was used to process the information. In order to monitor the groundwater level, data from underground piezometric wells in the study area were used for every 5 years with the interpolation method using kriging models, which have a very high accuracy. The K-Bessel method with an RMS value of 0.142 for 2019 and the Gaussian method with an RMS value of 0.129 for 2023, which were known to be the most accurate methods, were extracted. According to the results, the maximum subsidence rate in 7 years in the Shahriar Plain was estimated to be 28 cm and the uplift rate was estimated to be 10 cm https://hyd.tabrizu.ac.ir/article_21450.html Landslide susceptibility mapping is an essential part of landslide risk assessments. Subsequently, it helps to manage landslide loss reduction in an area. The primary objective of this research is to produce landslide susceptibility mapping using analyical hierarchy process and support vector machine models in the north of Tehran. Landslide distribution mapping and determine their location is the first step in this research. The maps of condiioning factors were prepared from different sources and entered into the GIS environment. Finally, the weights calculated from the AHP and SVM methods were imported into the ArcGIS software to create landslide susceptibility maps. The AHP-based prioritization of conditioning factors showed that elevation, slope, and distance to faults have the greatest impact on landslide occurrence in the study area. The ROC curve and Landslide Density Index (LDI) were used to assess the accuracy of the models. The findings indicated that the accuracy of the SVM model was very good, and AHP model was good for the study area. These results indicate that the conditioning factors for landslide occurrence were appropriately selected, resulting in satisfactory accuracy of the models used to generate the susceptibility map. To assess the overall susceptibility in the region the area of the four susceptibility classes were calculated for both models. The results showed that a large portion of the region is highly susceptible to landslides. The high and very high susceptibility classes identified priority areas for focused landslide management and mitigation efforts.