جستجوی مقالات مرتبط با کلیدواژه "landslide" در نشریات گروه "جغرافیا"

In recent years, due to heavy rainfall affected by climate change, global warming, and human activities, the occurrence of landslides and the reactivation of old landslides have increased, and every year in many countries of the world, landslides cause great damage to human structures such as buildings, roads, power lines, etc.There is no systematic information on the age, type, frequency, and distribution of landslides in the world, so the lack of this knowledge will have negative consequences. Therefore, preparing an inventory map of landslides, assessing the risk and hazard of landslides for areas with a high concentration of landslides is important for predicting and preventing future hazards.One of the applications of landslide inventory maps is in landslide hazard and risk studies. Landslide hazard and risk assessment is a complex operation that requires the combination of different geomorphological and geological techniquesThis research, by combining historical geomorphological data, remote sensing, and field studies with the proposed method of Cardinali et al. (2002), studies the hazard and risk of landslides in the Kalpush catchment and predicts the vulnerability of elements in order to plan and reduce future damage.

The Kalpush Catchment is located in the north of Semnan province and adjacent to Golestan province. This Catchment is located in the east of the forested heights of the cities of Galikash and Minoodasht in Golestan province.The research method used in this applied research and based on a systems approach using library, field survey and remote sensing methods.First, an inventory map of landslides was prepared from historical images and field survey. Then, the relative age of the landslide event, geometric and morphological characteristics, type of landslide, etc. were calculated. After collecting the above documents, the landslide hazard and risk assessment was carried out in 5 stages using the geomorphological method proposed by Cardinali et al. (2002).

109 landslides with an approximate area of 11 square kilometers were identified, which constitute 10 percent of the total basin. These landslides are concentrated in the south and southwest of the Kalpush basin. The landslides were divided into 4 classes based on the relative time of occurrence (54-year period), image dates, rainfall events of 2018-2019, and the decrease in the water level of the Kalpush dam lake in 2021: before 1968, 1968-2019, 2019-2021, and 2021-2022. 66 percent of the landslidesin2019-2021 and 55 percent of the landslides in 1968-2019 occurred on landslides older than 1968. All new landslides in 2021-2022 were also formed on the shores of the dam lake.According to the type of landslides in the Kalpush Catchment and based on the classification of Cruden and Varnes (1996), 98 percent of the landslides (107 cases), which include creep, rotational, translational, and lateral spreading landslides, have slow movement, and only 2 debris flows in the group of landslides in 2019-2021 have rapid movement. Therefore, almost all landslides in the Kalpush catchment have occurred with slow speed and movement from the past to the present.Deep landslides older than 1968, which were probably affected by different geomorphological, climatic, and earthquake conditions at the time of occurrence, had high intensity and relatively low frequency of occurrence at the time of formation and formed 8 landslide hazard zones. These zones have been reduced to 5 zones for deep landslides in 1968-2019 and 2019-2021, and the reactivation of landslides has less intensity and more frequency, so that they showed a variable landslide hazard and the landslide of Hossein Abad village recorded the maximum landslide hazard.The elements at risk of Kalpush and the location of landslides, the villages of Hossein Abad, Gushhe Degarman, and Korang, due to their high population and density and the roads leading to them, have a higher potential vulnerability hazard (V).The results of the landslide risk zoning map show that there is no area with very high landslide risk in the Kalpush basin due to the absence of rapid landslides (collapse, etc.). Also, Hossein Abad village is completely located in the high landslide risk zone. High landslide risk (R3) refers to areas with slow landslides with high intensity and frequency, high probability of structural and functional damage to structures and infrastructure, and less risk of death. Part of Karang village is located in the medium landslide risk zone (R2). Medium landslide risk (R2) refers to areas with slow or rapid landslides with mild intensity and superficial vulnerability. The results of the above discussions show that in the Kalpush basin, 9 landslide hazard zones (LHZ) with an area of 17 square kilometers have been identified in a concentrated manner in the south and west of the basin

The proposed geomorphological method is a specialized, accurate, and efficient method that has different responses in different locations. This method is based on the geomorphological observations of experts and the preparation of a multi-temporal landslide inventory map. If the older landslides, their morphological pattern in the region, and aerial photographs are evident, this method can be used. Also, this method is reliable and cost-effective for different scales of watershed, provincial, city, and village. As in this research, it provided the correct answer for the Kalpush basin and its villages.Therefore, it is suggested that given the complete presence of Hossein Abad village in the high landslide risk zone, the village be completely relocated to reduce the probability of vulnerability of elements at risk of landslides in the future. Also, this method is introduced and proposed as a reliable and compatible method for geomorphologists, engineers, and crisis management in the Alborz and Zagros slopes of Iran.

A major cause of landslide and relevant losses and fatalities is inappropriate land management, especially in mountainous areas and valleys. In this study, risk-vulnerability superimposed model was used to investigate the probability of occurrence of fatal landslides in Tarom Watershed. The risk-vulnerability superimposed model has been formulated to evaluate the landslide risk and vulnerability. These factors include topography, geology, number of streams, rainfall, frequency of faults, land use, density of roads, population density, and construction density, which were considered and analyzed in the present model. According to the model output (i.e., risk-vulnerability map), it was figured out that the eastern and northeastern parts of Tarom Watershed are exposed to highest levels of risk and vulnerability. That is, occurrence of a fatal landslide with financial losses is more probable in these areas, as compared to other parts of the watershed. Results of the present research showed that 17%, 35%, 23%, 16%, and 9% of the entire watershed area were classified as being exposed to very low, low, moderate, high, and very high risk-vulnerability levels, respectively. Advantage and superiority of this model over other models and methodologies for landslide study is simultaneous consideration of landslide occurrence risk and vulnerability of the study area to the landslide, making it capable of determining the areas of higher probability of fatal landslides with financial losses.

In the current study, the risk of landslides in the Zamkan Watershed, located in Kermanshah Province, was evaluated. Two machine learning models, Support Vector Machine (SVM), and Logistic Regression, were used to prepare a landslide susceptibility map. Toward this, 13 informational layers including elevation, slope, aspect, Melton ruggedness number, terrain convexity, stream length, valley depth, topographic wetness index, precipitation, geological formations, distance from rivers, distance from roads, and vegetation cover were utilized as independent variables. Approximately 70% of the watershed's landslide pixels were used for model training, and 30% for model validation. Model validation was performed using ROC curves. The results indicated the higher performance and accuracy of the radial basis function (RBF) kernel of the SVM model for generating landslide hazard maps in the study area. The area under the curve (AUC) for the RBF kernel was approximately 0.951 for model training and 0.944 for model testing. The results suggest that slope with a coefficient of 0.28, precipitation with a coefficient of 0.27, lithology with a coefficient of 0.26, and elevation with a coefficient of 0.22 are the main controlling factors for landslides occurrence in the Zamkan Watershed. Both the SVM model and logistic regression confirmed the deterministic effects of selected factors on landslides. About 35% of the study area as classified as highly susceptible to landslides, primarily in the eastern half of the watershed. Factors such as high elevation, steep slopes, heavy precipitation, and the Kazhdomi Formation's composition were identified as key contributors to this susceptibility.

The analysis of some geological features and structures can be used to determine Quaternary developments. Analyzing the types of landslides, their density, and scale is the key to identify the evolution of landforms. Based on this, the current research was carried out with the aim of spatial analysis of landslides that occurred in different lithologies of Mo’alem-Kelayeh basin, a part of eastern Alamut basin, between the longitudes 50°26'00″ to 50°31'20″ and the latitude 36°22'00″ to 36°30'00″, based on topographical, geological conditions, vegetation, the condition of waterways, and the proximity of different rocks in the area. According to the results, the Karaj, Rute, Shamshek and Neogene and destructive sediments are the most erodible formations in the studied area, which are the most important factors involved in the occurrence of mass movements on a micro and macro scale under the Mo’alem-Kelayeh basin. The nonresistant lithology is more extensive in the geographical levels downstream of the rivers. In such areas, in addition to the loosening of the lithology in wide sections, the material and energy flowing in the river also increases, if due to the lower slope of the slopes, the effect of the river on movements of the slopes becomes more limited. In the terrestrial levels close to the ridge line, matter and energy are less and lithology is more resistant; But due to greater slope of the slopes and the involvement of physical weathering in the porosity of rocks, the effect of river on the occurrence of surface slope movements increases.

 Statement of the Problem: 

Landslides are among the most important geomorphological hazards in mountainous areas. Large-scale landslides often have significant social and economic impacts, making them one of the costliest natural disasters worldwide, causing considerable damage to individuals and infrastructure each year. Nowadays, due to climate change and human activities, the frequency, intensity, and impact of landslides have increased, posing significant threats to life and property. In recent years, with rapid socio-economic development, the spatial extent of human activities has also gradually expanded. The occurrence of heavy rainfall has increased the frequency of landslides in mountainous areas and the resulting economic losses have gradually increased, turning landslides into barriers to human environment development. Given the complex physics and diverse nature of triggering mechanisms (such as rainfall and earthquakes), dynamic landslide monitoring is vital for risk assessment and management. While small landslides constitute the majority of landslide events annually, large landslides are usually responsible for a significant portion of casualties and damages. In most parts of the world, identifying their locations and effects still faces many challenges due to the diversity of landslide types and morphologies, as well as the difficulty in collecting and updating survey forms. Therefore, the development of tools and techniques for identifying and monitoring these widespread gravitational processes is of great importance. Spatial assessment and preparation of hazard maps are fundamental strategies in landslide risk management. In the present study, spatial assessment and zoning of landslide risk in the Zemkan watershed in Kermanshah have been addressed. This watershed drains parts of the western slopes of the Zagros Mountainous area and has a high potential for landslides.

In order to assess the spatial risk of landslides in the watershed, studies of 13 factors affecting landslide occurrence were conducted, including elevation, slope, aspect, the Melton ruggedness number, surface convexity, length of slope, valley depth, TWI, precipitation, geological formations, distance from streams, distance from roads, and vegetation cover. In line with the research objectives, the necessary data were obtained from 1:25,000 and 1:50,000 topographic maps; 1:100,000 and 1:250,000 geological maps; Aster satellite images with a resolution of 27 meters; Sentinel satellite images (with a resolution of 10 meters) and Google Earth (GeoEye with an approximate resolution of 1 meter); climate data; field studies; and library resources. ArcGIS, ENVI, and SAGA GIS software were used for the preparation and preparation of thematic layers and the execution of research models. A catastrophe theory-based approach was employed for zoning and spatial prediction of landslide occurrence risk in the Zemkan watershed area. Functions of catastrophe theory were used in GIS environment to combine and overlay these factors. The model employed was used to address uncertainties associated with decision-making and data classification. In this model, the weight of criteria is determined by the internal mechanism of the system, which has a mathematical nature.

The results of the research demonstrate the satisfactory performance of catastrophe theory functions in zoning the risk of landslides. The model accuracy, calculated using field mapping of landslide zones and receiver operating characteristic (ROC) curves, was approximately 90%. Catastrophe theory-based modeling showed that slope factors with a coefficient of 1.3, precipitation with a coefficient of 1.2, elevation with a coefficient of 1.1, and geological formations with a coefficient of 1 are the most important variables affecting landslide occurrence in the Zemkan watershed. Over 32.4% of the study area fell into high and very high susceptibility classes. Several conditions have led to significant portions of the central and southern parts of the Zemkan watershed having a high landslide potential. In this regard, factors such as high elevation and slope, significant precipitation, the presence of landslide-sensitive geological formations such as marl, clayey limestone, and shale (Kazhomi formation), westward orientation perpendicular to westward winds, long slopes, high drainage density, and human interventions are noteworthy.

In the present study, a model based on catastrophe theory was utilized to mitigate uncertainties associated with decision-making and data classification. In this model, the weights of criteria are determined by the internal mechanism of the system, which has a mathematical nature. The results of the model are repeatable and generalizable, thereby reducing decision-making uncertainties and enabling the integration of multiple quantitative and qualitative data. The research findings indicate the effectiveness of catastrophe theory functions based on GIS in spatial assessment and zoning of landslide risk in the study area. The alignment of landslide field mapping with landslide risk zoning maps and receiver operating characteristic (ROC) curves indicates a model accuracy of approximately 90%. The model results show that slope, precipitation, elevation, and geology are the most important factors influencing landslide occurrence in the Zemkan watershed. A significant portion of the study area falls into high and very high susceptibility classes of landslides, indicating a considerable potential for landslides in the Zemkan watershed and the need for planning to mitigate the risks associated with landslide occurrence.

Landslides represent a significant natural hazard, causing substantial damage and economic losses worldwide. Accurate landslide susceptibility assessment is crucial for mitigating these risks. This study employs a Geographic Information System (GIS) and the Analytical Hierarchy Process (AHP) to investigate and map landslide susceptibility in the Kiasar 1:100,000 quadrangle, Iran. This study employed a comprehensive set of influencing factors to assess landslide susceptibility including geology, slope, aspect, precipitation, seismicity, faults and folds, distance to roads, distance to rivers, erosion, and land use. Among the selected criteria, precipitation and slope were assigned the highest weights of 0.27 and 0.22, respectively, reflecting their significant influence on landslide occurrence. Conversely, drainage and land use received the lowest weights of 0.034, indicating their relatively lesser impact. The study findings revealed that approximately 6% (151.68 square kilometers) of the total study area (2500 square kilometers) is classified as susceptible to landslides. This corresponds to 22% of the total area occupied by villages within the investigated region. Furthermore, field verifications confirmed that the main power transmission lines and primary oil pipelines are not exposed to landslide hazards. However, some mines within the study area were identified as being at risk. Within the study area, two industrial facilities – a bakery and a fruit preservation plant – were identified as being located within landslide-prone zones. The high correlation between historical landslide occurrences and the methodology employed in this research suggests that the adopted approach is well-suited for landslide susceptibility mapping in mountainous regions characterized by climatic and vegetation diversity.

Land evaluation for the purpose of recognizing and zoning areas sensitive to range movements, especially landslides, is one of the researches of natural geographers, especially geomorphologists. The study and investigation of the phenomenon of landslides in the northern regions of the country, especially the steep mountainous regions, is very important due to heavy rainfall, incorrect land use and erosion-sensitive formations. Identifying and determining sensitive and prone to landslide areas can provide the basis for the implementation of slope stabilization plans while preventing damages.In this research, in order to zone the risk of landslides in the Ziarat watershed, Analytical Hierarchy Model (AHP) was used. The variables used in this research include slope, slope direction, geology, land use, distance from the road, distance from the watercourse, and rainfall points. Also, in order to better understand the landslides in the region, their relationship with the distance factor from the fault was investigated, which showed that this factor did not have a great impact on the landslides in the region. The natural factors of the region had the most important effect on the occurrence of landslides in the basin.According to the importance of these factors, it includes the distance from the waterway, the land use that most of the landslides have occurred in forest lands, and the geological factor of the basin, which among the different formations, the Jiroud Formation has had the most landslides due to its lithological conditions. In the investigation of the relationship between landslides and natural factors, we found that most of the landslides occurred at an altitude of 500 to 1000 meters on a slope of 15 to 30 degrees and also in the east and west directions of the basin, and most of the area of ​​the Ziarat watershed is in The area was placed at high risk.

One of the hazards that threatens the existing infrastructures in different regions is the phenomenon of landslides. The present study tries to identify the prone areas of this natural phenomenon in Khalkhal city, which was done using the neural network method. For this purpose, 9 factors affecting landslides were identified and prepared, the layer of landslides that happened, obtained from aerial photos and satellite images and field visits, and using non-sliding points in the region, perceptron neural network training data. They created several layers. In order to model the neural network, these data were transferred to the MATLAB 2016 software after initial preparation in the ARC GIS 10.5 software environment and were trained using MLP neural network coding to deal with the data that they have not encountered. , make predictions. The structure of the designed neural network was selected from among the many networks that were created and tested, 1-12-9, which obtained 9 inputs as the number of effective criteria, 12 neurons in the middle layer and one neuron and layer for the output of the network. The results of the validation chart of the neural network (ROC) model show a high accuracy of 95% of the created model in predicting sliding pixels. According to the results, 0.57%, 0.11%, 0.07%, 0.06% and 0.07% of the studied area were placed in very high, high, medium, low and very low classes, respectively. Due to the fact that the largest area of the region is in a very high risk class,

This study aimed to systematically explain the potential of the landslide occurrence to provide a prediction model of the possibility of this phenomenon in the Yamchi catchment in Ardebil province. In this regard, both approaches of discrete and continuous variables were used by means of overlay and logistic regression, respectively. Independent variables included elevation, slope, aspect, lithology, annual rainfall, roughness, general curvature, topographic wetness index, vegetation index, distance to fault, distance to stream and distance to road. The results, firstly, revealed the areas with high landslide potential by the matching layers of independent variables with the landslide layer in the geographical information system (GIS). These areas were in the middle elevation, high slopes, northern slope, high roughness, erodible formations, high rainfall, medium vegetation, surroundings of faults and rivers. Secondly, the results of the logistics regression model by providing a prediction equation of probability of landslide occurrence showed that the resulting model with pseudo r2 and ROC 0.22 and 0.86, respectively, had good power and efficiency to predict landslide through the catchment. In addition, the resulting beta coefficients for independent variables indicated that the importance of the variables was as follows: vegetation index distance to road, rain, lithology, distance to fault, elevation, topographic wetness index, roughness index, aspect, slope, and distance to river. In the end, the need to pay serious attention to the supporting and protection of vegetation cover of the mid -range and upstream of the catchment was determined because of unstable geomorphic conditions of these areas.

Text Landslides are one of the types of large-scale processes that cause many human and financial losses in many parts of Iran and the world every year. The increase in population and the expansion of human settlements in mountainous areas, the difficulty of predicting the occurrence of landslides and the numerous factors influencing the occurrence of this phenomenon, reveal the necessity of landslide risk zoning. Identifying the effective factors in the occurrence of this phenomenon and its risk zoning is one of the basic and practical methods to achieve its forecasting, control and monitoring solutions. By using field studies, geological and topographical maps, and by reviewing the researches and studies done in this field, as well as examining the existing conditions in the studied area, 9 factors of elevation, slope, slope direction, lithology, distance from the fault. , the distance from the river, the distance from the communication roads, land use and rainfall were investigated as factors affecting the occurrence of landslides. Therefore, the purpose of this research is to investigate and analyze the most important factors involved in creating the risk of landslides in Garami city and to identify the prone areas that will probably be involved in landslides in the near future. In this research, the zoning of prone areas was done with the Aras multi-criteria algorithm in the Edrisi software environment, and according to the results of landslide risk zoning; The criteria of land use, slope, and lithology are the most important factors involved in creating the risk of landslides in the study area with weight coefficients of 0.187, 0.152, 0.152, and 0.142, respectively, and are 361.99 and 450.32, respectively. A square kilometer of the area has a very high probability of danger. Finally, it can be said that the most important factor involved in increasing the amount and potential of landslides in Germi city is the change of land use and the increase of agricultural land and livestock pastures.

The area under study (Tehran gas line 3) is one of the most important and sensitive areas in terms of threats to gas supply due to the instability of domains and tectonic activities. In this research, to achieve the research objectives, different data from different sources and different criteria were used. For example, geology, height, distance from the slope fault, slope direction, distance from the river, land use, soil, distance from the road, precipitation, land cover, and height were used. Risk assessment using five fuzzy models, network analysis, Fuzzy network, multilayer perceptron, and random forest method were analyzed. Because the traditional methods of risk assessment are based on mathematical functions and need more knowledge of experts and are less practical, intelligent systems were used which, in addition to being easy to use and analyzing relationships, provided more appropriate results. The results of the investigations showed the comparison of landslide and earthquake risk with different models shows that the risk of landslide is higher with the RF model. Still, with the use of the ANP model, the studied area shows relatively higher risk. In the Fuzzy model, the highest percentage belongs to the low-risk class and the lowest percentage belongs to the medium-risk class. In the fuzzy-ANP model, the relatively high-risk class shows the highest percentage and the high-risk class shows the lowest amount in the range. In the MLP model, most of the study range has medium risk and the high-risk class range has the lowest amount in the range. Therefore, in the RF model, the highest percentage belongs to the relatively high-risk class and the lowest percentage belongs to the low-risk class. According to the evaluations made from the results of the ANP model, the systematic error (MBE) of this model is -0.20336 and the absolute error of the model is 0.209895. The RMSE error was 0.131107. According to the evaluations of the results of the Fuzzy model, the systematic error (MBE) of this model is -0.23687 and the absolute error of the model is 0.25511. The RMSE error rate was 0.162122.

Risk assessment in threats that have a spatial aspect and are related to the characteristics of the environment and the pipeline bed is considered essential. Due to the nature place of risk, it seems that it is possible to establish a connection between the process of risk estimation and the geographic information system, and by using different models, the high-risk areas can be specified. According to the issues, in this research, the process of environmental risk estimation has been investigated with a geographic information system and using hybrid-fuzzy algorithms. After positive results, by assessing the risk of gas pipelines, valuable information such as risky components can be determined and a suitable reaction and strategy can be used to reduce and even eliminate it. To achieve the goal, the appropriate technique has been used in the research. which can assess the existing risks more accurately and reliably. Also, planners and managers should act with a wider horizon and a lower risk factor toward the optimal management of gas transmission lines.

In this research, five models were analyzed for risk assessment. These models were fuzzy analysis, network analysis, fuzzy network, multi-layer perceptron, and random forest method. Given that the traditional methods of risk assessment are based on They are mathematical functions and lack the knowledge of practical meter experts, intelligent systems were used which, in addition to easy use and relationship analysis, provided more appropriate results. In this research, the network analysis method was used. The Multilayer Perceptron (MLP) model was also used in this research. This method requires less investigation in estimation or statistical methods to analyze the accuracy of data. The most important advantages of MLP are high learning potential, robustness against noise, non-linearity, parallelism, error tolerance, and high capabilities in task generalization. The overall goal of this model is to find a system to minimize the total error for the relevant training data by The training algorithm. The networks between these layers are with different weight values in the interval [1 and -1]. The result of input values, weighted values, and bias values are obtained from equation (1)based on the aggregated performance.
Equation (1)                                               
n shows the total number of input points, I_(i) the input variable, β_j the bias value, ω_ij the connection weight.
Random forest model (RF) was another model used in this research. In the structure of the RF method, the importance of the variables was determined in the model and the variables that have a greater role in each tree and the final model were identified. It was considered from the bag and these data played the role of experimental data to evaluate that tree. Based on this model, the most weight is given to the rainfall criterion and the least to the soil criterion. The measured values were used in the test stage. To validate the model, the statistical indices of root mean square error (RMSE), mean error of exploitation (MBE), and mean absolute error (MAE) were used. The relations of these indices are in the form of equations 2 to 4:
Equation(2)                 RMSE =       Equation (3) MBE =                Equation(4) MAE =                
In the above relationships, O_i and P_i are the observed and estimated values at time i, and t is the number of days.

In this study, it was tried by using different models, while the level of risk is checked, a comparison is also made between the models. In the scope of the study, The comparison of landslide and earthquake risk with different models shows that the risk of landslide and earthquake is higher based on the RF and ANP models. The analyses conducted by other researchers show that the MLP model and the ANES model show better results. The results show the difference between the output values of the model and the values. The result of smart tracking is as target points for model evaluation. The MBE error shows whether the model error is generally positive or negative, that is, whether the model estimates the data more than the real values or less than the real values. This error includes any systematic errors in the design, collection, analysis, interpretation, and dissemination of data that lead to incorrect estimates. This error is one of the systematic errors in that the increase of sample points does not affect the reduction or increase of the error. The systematic error (MBE) of this model is 0.002812.

In the ANP model, parts of the pipeline based on earthquake criteria are 0.363 km long with a low vulnerability is 20.317 km. Also, 258.60 km is with relatively high vulnerability and 29.062 km is with high vulnerability. Based on the results of the review of landslide criteria of the ANP model, 0 % of the area is in the low-risk class, 17.28% is in the risk class average, 73.14% in the relatively high class, and 9.58% in high-risk class. According to the landslide criterion using the ANP 008 model, 19.19 km of the studied area with medium vulnerability, 80.454 km with relatively high vulnerability, and 10.538 km with high vulnerability. According to the landslide index results of the MLP model, 9.78% of the area is in the low-risk class, 47.17% in the medium-risk class, 36.95% in the It is relatively high and 6.10% is in the high-risk class. According to these results, most of the region is in the class with relatively high risk. Based on the results of the MLP model earthquake criteria, it was found that 5.093 km with low vulnerability, 29.48 km with moderate vulnerability, 713.7 km 42 km with relatively high vulnerability, and 32.714 km with high vulnerability. Therefore, according to the results, it can be said that in areas with high risk, it is necessary to use higher class pipes, periodic studies and investigation of physical factors and an environment that is effective in causing damage should be done to reduce their vulnerability.

Landslides can occur due to human activities and environmental factors that have destructive effects. Reaching zoning of the risk of this phenomenon can help to make decisions to reduce losses and damages caused by this phenomenon. In this study, to prepare a landslide risk map, various natural factors including height, slope, slope direction, precipitation, soil type, distance from the waterway and distance from the fault have been used. Also, the historical information on the earthquake occurrence has been used to estimate the effect of each criterion and sub-criteria. For this purpose, by using the frequency ratio (FR) method, the impact of sub-criteria and by using Shannon's entropy method, the weight of each criterion has been calculated. Also, after calculating the weights of criteria and sub-criteria, VIKOR and weighted average (OWA) methods have been used to prepare a risk map. Based on the obtained results, the criterion of soil type with a weight value of 0.33 has the most effective weight. To compare different methods, the FR criterion has been used, which based on the results obtained, the OWA method has better results for α values ​​equal to 10 and 2.

In this research, the spatial assessment of the risk of landslides in the Ligvan-chai watershed was done. Liqvan Chai watershed with an area of about 200 square kilometers is located in the political-administrative area of Tabriz city. In order to zone the risk of landslides in the Liqvanchai watershed, nine spatial variables were used. To determine the weight of each of the variables, the Analytical Network Process (ANP) model was used in the framework of the Geographical Information System (GIS). Simultaneous attention to the internal and external relationships between the criteria is the most important advantage of the ANP model compared to other multi-criteria decision making models. The results of the ANP model show that the three variables of slope (with a weight of 0.279), geological formations (with a weight of 0.224) and precipitation (with a weight of 0.165) are the most effective variables involved in the occurrence of landslides in The scale of the watershed is Liqvan Chai. In fact, these variables control the occurrence of landslides in the region by providing favorable conditions. Also, zoning the risk of landslides by combining the research criteria using the coefficients obtained from the ANP model in the framework of the Geographical Information System (GIS) showed that in about 36% of the basin level in the high risk class and It is placed very much. Most of these areas are located in the lower reaches of the basin. The simultaneous participation of several factors, such as high slope, high rainfall, sensitive geological formations, the presence of faults, etc., has made the upstream parts of Liqvanchai basin have suitable conditions for the occurrence of landslides.

Mass movements of materials are divided into slip, flow and creep, among of which landslides always cause heavy financial and economic losses worldwide. In this study, based on the analysis of landslides in Shahroud Basin, effective vectors were rated. Using ArcGIS software, the spatial distribution of 183 landslides in Shahroud basin was analyzed and interpreted in fault, waterway, road, lithology, slope, aspect, precipitation amount and height. According to the effect of different vectors, and the spatial distribution of landslides in Shahroud basin, points were scored and entropy matrix was adjusted for them and landslide occurrence in Shahroud basin was zoned. Based on the results of applying Shannon entropy model in Shahroud basin for landslide zoning in terms of landslides, high-risk zones accounted for 41.79% and an average of 52.76% of the basin area. The adaptation of landslide risk zones of Shahroud basin shows the concentration of 74.86% of landslides on slopes of 10-40°, 64.97% in marl, conglomerate, and basalt lithologies, 87.97% at altitudes 2123-823m, 85.79% in semi-arid and temperate rainfall classes (685-315mm) on land surfaces with slope in the western direction, 55.74% at a distance of 0-300m from the river, a distance of 0-500m from the fault and at a distance of 1000-500m from the road. The concentration of danger and landslide zones in the two central parts of Taleghan subspecies and after the joining of Alamut and Taleghan rivers and the formation of Shahroud River is more than other areas. Such changes in landslide distribution due to nonlinear compliance of this geomorphological event are influential factors. The occurrence of landslides is a function of the interaction between factors rather than their abstract effects.

One of the types of domain instability that causes financial and human losses every year is the risk of landslides, which has social, economic and environmental consequences. The basin of Balharud Shahrestan is one of the areas prone to landslides due to its mountainous nature and steep slopes. Investigating the effective factors in the occurrence of landslides and identifying the prone areas is an important step in managing natural resources and achieving sustainable development. Radar interferometric technique is an effective method in measuring ground surface displacement. This technology has become very common in the investigation of natural hazards of the earth, such as mass movements of slopes, subsidence, earthquakes and volcanic activities. In this research, in order to identify and measure landslides, Sentil 1 radar images of 2020 and 2022 have been used. In order to process information, SARSCAPE software has been used, and the maximum landslide displacement in the opposite direction was estimated to be 0.154 cm, which was observed along the fault lines and in the center of the study area. Also, the results of this research showed that radar images have a good potential for revealing the instability of domains and calculating their displacement.

In this researchlandslide sensitivity was zoned using statistical models intheKurdistan Dam watershed and the most appropriatemodel was introduced.First, the studied area was determined and with field observations, the number of 9 landslides was recorded and a landslide distribution map was prepared. In the next step, the factors affecting the occurrence of landslides including geology, rainfall, land use, distance from the river, distance from the fault, slope and height were identified and then a map of these factors was prepared. To determine the rateof each of the effective factors in the occurrence of landslides, the map of each information layer of the effective factors is integrated with the distribution map of the landslide and using AHP, BWM and FUCOM statistical modelsseparate information layers are weighted and By overlapping different layers, the final landsliderisk zoning mapswere prepared and compared.The results showed that land use in AHP and BWM methods and rainfall lines, in addition to land use inFUCOM method have the greatest effect and the criteria of heightdistance from the fault and slope respectively in the three AHP, BWM and FUCOM methods have the least effect on the occurrence of landslidestheresults showed that the lithological variable has a great role on the occurrenceof landslides in the studied area.In generalthe results showed that in AHP and BWM methods, the numberof required pairwise comparisons increases significantly with the number of compared parameters, and in this case, the uncertaintyof opinions increases, which shows the superiority of the FUCOM method over It showswell in other ways.

Landslides is considered as one of the major natural hazards in mountainous areas. Identifying the effective factors in the occurrence of landslides in a basin and zoning its risk is one of the basic tools to achieve solutions to control this phenomenon and select the most appropriate and practical management option. The present study aimed to evaluate the factors affecting the occurrence of landslides and its zoning using the LIM model. For this purpose, after obtaining the weight of each of the factor maps and the weight column of each unit (including: altitude, slope, slope direction, drainage density, distance from the river, distance from the road, lithological units, distance from the fault, Vegetation, land use, annual rainfall and soil hydrological groups) The final weight and landslide zining map was obtained using a model from the algebraic sum of the 11 factor layers. According to the results, arounf 34 % of the study area located in the range of high to very high risk, around 41 % in the range of low to very low potential risk, and the rest in the range of medium potential of landslide occurrence. The results of the maximum values obtaned for the final weight of different variables indiated that slope, distance from the road, land use, vegetation, height, lithology, distance from the river, rainfall, soil hydrological group, slope direction and the distance from the fault have the most to the least influence on the occurrence of landslides in the studied region, respectively. In general, the research results can provide useful information to planners in various sectors, including agriculture, industry, environment, etc.

Landslide can be defined as mass movement of materials on sloping slopes under the influence of mass gravity and triggering factors such as earthquakes, floods and heavy rains. This phenomenon is one of the natural hazards that causes a lot of loss of life and money every year in mountainous, rainy and seismic areas. By zoning the risk of landslides, it is possible to identify sensitive areas with high potential for landslides and by providing solutions, appropriate control and conduting effective management methods, the occurrence of landslides can be partially prevented or the damages caused by them can be reduced.

In this research, the effect of the rainfall system of April 2018 on the occurrence of landslides in Boyer Ahmad and Dena counties in Kohgiluyeh and Boyer Ahmad province has been investigated. The zoning of landslide risk has been performed using Haeri-Sami experimental model and the integration of thematic rock maps based on geology, slope angle, length of fault, length of road and river, amount of rain, intensity of rain and earthquake have been done in the scale of 1:1500000. After preparing the landslide zoning map, by carefully examining the Google Earth images, the areas of the studied areas where the potential of landslides were expected to be higher were identified. Then, during the extensive field visits, the geographical coordinates of the observed landslides were recorded and by preparing the point coordinates of the landslides in the Arc Map software environment and transferring them to the Google Earth images, the location of the landslides was recorded. The identification and range of each location was prepared in the form of a polygon file and transferred to the Arc Map software environment, and in this way a digital map of the distribution of landslides in the studied area was prepared. The density ratio index (Dr) was used to evaluate the landslide risk zoning map.

The results showed that only in terms of the geological factor, about 23% of the area under study has a high sensitivity to the occurrence of landslides, while other factors including the slope angle (73%), fault length (99%), and length of roads and rivers (98%) have little sensitivity to the occurrence of landslides. In terms of the amount of rainfall, in the average rainfall mode (78% of the region) it has moderate sensitivity and in the mode of April 2018 rainfall, 82% of the region is of medium sensitivity and 10% of high sensitivity with regard to the occurrence of landslides. In terms of the intensity of rainfall (79%) and earthquakes, about 58% of the area under study has a medium sensitivity to the occurrence of landslides. Landslide risk in 4 classes: no risk, very low risk, low risk and medium risk, respectively, have an area distribution of 15.1, 36.07, 43.33 and 5.5% in the case of average rainfall and 22.54, 7.9, 38.03 and 31.53% in the case of April 2018 rainfall. In the average rainfall, about 94% of the studied area is in the classes without risk to low risk of landslides, and in the rainfall of April 2018, about 69% is in the low and medium risk range.

Based on the results of landslide risk zoning, it can be said that the occurrence of landslide risk in the study area based on the studied model and the mutual conditions of the effective parameters in the average rainfall is almost low and in the rainfall of April 2018, the risk is low to moderate. Therefore, it can be concluded that the rainfall in April 2018 has a relatively significant effect on the risk of landslides. In connection with the evaluation of the efficiency of the model using the density ratio index, the model has been able to separate the landslide risk zones well, and in the very low risk class, the amount of rainfall is more accurate in both cases.

Landslides are a critical geohazard, often triggered by seismic activity, intense rainfall, and water table fluctuations. Understanding the complex interplay of these factors is a key task in hazard assessment and mitigation strategies. This comprehensive study investigates the regional potential for water flow dynamics, displacement mechanisms, and landslide genesis. Based on a dual approach of rigorous fieldwork and meticulous laboratory analysis, approximately 5 kg of fine-grained clay and 100 kg of coarse-grained material were collected for in-depth mechanical property testing. These investigations focused primarily on assessing Atterberg limits and shear strength characteristics. The results showed a striking correlation between the water table and the site's structural integrity. In particular, the marl and alluvial layers exhibited a significant decrease in resistance, ranging from 40% to 55% and 60% to 80%, respectively, in different regions of the study area. In addition, the cohesion of the layers decreased with increasing slope steepness, resulting in a reduction in internal friction angles. This empirical evidence highlights the region's susceptibility to increased landslide risk, particularly in precipitation-induced surface water infiltration and potential seismic upheaval, such as the powerful 7.3 magnitude earthquake in Sarpol Zahab in 2017. These combined factors highlight the imminent threat of landslides and call for proactive risk management and disaster preparedness measures.

Landslide phenomenon is a kind of danger in mountainous areas, which has always caused a lot of damages in Iran. Therefore, identifying areas that are prone to landslides can prevent the occurrence of this phenomenon and save human life and property. The purpose of this research was to prioritize the effective components in the occurrence of landslides and zoning the risk of its occurrence in Bagh Malek county. For zoning the risk of landslides, 9 factors were considered, including: slope, geology, water drainage network, height, rainfall, land use, fault, slope direction and road. The method used in landslide zoning is AHP. Geographic information system (GIS) software was also used to combine the layers. In the Hierarchical Analysis Process (AHP) method, the weight of each of the nine factors was calculated and applied to the desired layers according to the importance of each of them in the occurrence of landslides, and by stacking the layers, a landslide risk zoning map was produced. The results showed that the factor of slope, land type and surface water network with the highest rank of 1 to 3, and the road and slope direction with the 8th and 9th rank have the least impact on the occurrence of landslides in this region. Low risk areas have 43.25% and high risk areas have 4.01% of the area. The results showed that most landslides in the region are related to the slope and type of land. The most sensitive parts of the region to landslides are the southwest and parts of the east and west of the region, and the most resistant parts of the region are some parts of the north, northwest, and some parts of the east of the region.