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

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.

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.

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.

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.

Communication networks are exposed to many and varied environmental risks due to their spatial extent and placement in environments with different specializations. One of the important natural hazards that threaten communication roads, especially in mountainous areas, is slope instability and landslides. The instability of the slopes during the construction of roads shows more frequency due to disturbing the balance of the slopes. Therefore, the identification and analysis of landslides in communication networks and roads can be very effective considering the hazardology aspect of these environments and geographical spaces. The studied route is a part of Baneh-Sardasht road between Sardasht and Darsawin. Part of the route of this old road was changed due to the new Sardasht (Kulse) dam that was built on the Zab river. Considering that various factors and parameters effective in the occurrence of geomorphological processes such as landslides react and act in different ways on the surface of geomorphological units, Analyzing the geomorphic changes of the study path from the perspective of landslides and determining the role of various effective parameters in creating this process is one of the main issues of this research, which has been systematically evaluated and analyzed.

The general method of this research was based on theoretical and library studies along with multiple field studies as well as statistical and software analyzes based on the fuzzy model. For this purpose, topographical and geological maps of the region as well as Aster and Landsat8 satellite images, GPS device, laser meter, Google Earth images, Arc GIS 10.3 software and spss18 software have been used. The model used in this research was the fuzzy model. First, effective parameters in landslides were identified, then they were fuzzified using fuzzy membership functions. In this research, the linear membership function is used, which has 4 parameters that determine the shape of the function. First, layers were created for the effective variables in domain instability, then in order to determine the membership of the layers in the degree of slope instability, the fuzzy membership function of each layer in the model, based on the type of relationship that each parameter has with slope instability in the study area and based on the relationship (Graeme, 1994) was determined. In this method, the area under the curve (ROC), with values between 0.5 and 1, is used to evaluate the accuracy of the desired model. The ideal model shows an AUC value close to 1, while a value close to 0.5 shows the inaccuracy of the model.

The final results of fuzzification of the variables showed that 16 variables were effective in creating instability and landslide hazards with different degrees. In connection with the zoning of landslide risk and its analysis, zoning and analysis was done based on Gamma 0.9. The results showed that the majority of the first part of the road from Sardasht to the Three- Way Qalte road and the second part of the Three- Way Qalte road up to Briso village (the new road) are located in the zone with medium to high and very high sensitivity. The results showed that the lowest level of sensitivity is in the third section, which is caused by the less influence of the effective variables, especially the role of lithology and the morphological unit, along with the increase in the distance from the effective variables, such as the distance from the river and fault, etc. The results of the analysis showed that the value of the area under the curve is equal to 0.811, which indicates the accuracy and efficiency of the used model. The overall results showed that the second part has the highest rate of landslides and the third part has the lowest rate of landslides, while the rate of landslides is high in the first part. The available results are in good agreement with the prepared landslide risk map in most parts, which indicates the accuracy of the study.

The mentioned research was related to the evaluation and zoning of landslide risk in a part of the Sardasht-baneh road centered on the new road. The final analysis showed that in the first boa, the role of variables of height, slope, precipitation and flow power in the process of slope instability corresponding to the road surface was significant. In the second part, the role of rainfall variables, distance from the road, distance from the fault and lithological conditions have been very effective in the instability process. The second part has shown a typical example of instability in the last few years, which indicates the landuse change in line with the construction of a new road after the construction of the Sardasht (Kulse) dam, which is on the surface of the sediments of the young terraces and in the immediate vicinity of the river. The greater stability of the third part is also related to the placement on the surface of the sediments of older terraces with greater resistance and also the further development of forest covers. Therefore, it is necessary to identify geomorphic units and determine their assignments, in other words, to recognize the form and process in environmental activities by humans.

Landslide is one of the most important and significant phenomena in environmental issues, watershed management and natural resources. The present study, due to the nature of the issue and the subject under study, is descriptive-analytical and in terms of applied studies with emphasis on quantitative methods. For spatial analysis of landslide susceptibility, criteria such as: (slope, slope direction, land use, distance from residential centers, land curvature, precipitation, height, vegetation density, geology, soil, waterway density, distance from the road in question In order to calculate the density and amount of vegetation, the product (Mod13Q1) of Terra satellite mode sensor was used and the final output was calculated by the overlap, all-link method in GIS environment.All processes and data analysis in the environment (Spss, Excell) and software (GIS) and curve (ROC) was used to evaluate and validate the models.The final results of the landslide susceptibility map show that the 5th floor with a high slip coefficient of %13.90 Of the total area, the 4th floor with a high slip coefficient of %17.89, the 3rd floor with an average coefficient of %33.90, the 2nd floor with a low coefficient of %22.91, the 1st floor with a very low coefficient of %11.40 have been allocated as a result of% 31 /79 of the total area is prone to severe landslides, as well as villages in high-risk areas, including villages (Ghezelgh range, Shakar Ali range, Khairat, Nomal, Tuskestan, Feyzabad, Maryam Abad).

Landslide is one of the environmental hazards that every year cause loss of life and financial losses in large parts of the mountainous regions of Iran. Therefore, identifying important and effective factors for the occurrence of this phenomenon can be used as a practical tool in reducing possible losses.  Modeling the susceptible area into landside hazards can be a useful tool for the police makers for protecting the prone areas. The Kameh watershed in the south of Isfahan province has been selected as one of the areas prone to landslides due to its special topography, geology, lithology, climate, and social-economic situation. This research aims to identify the effective factors in creating the phenomenon of landslides and to determine the areas with a high risk of landslides by applying the CART and TreeNet models with using of SPM and ArcGIS 10.8 software in the studied area. We have used 11 factors that affect the occurrence of landslides including geology, amount of precipitation, slope, distance from the river, TWI, land use, NDVI, elevation, aspect, distance from the faults, and distance from roads, as the independent variables and 311 landslides (as a polygon) collected from the study area were used as dependent variables. For running our models, 70% of the landslides were used as training and the remaining 30% of the landslide points were used for validation. The results of this study, according to the various statistical indicators that have been applied, show a higher accuracy of the CART model than the TreeNet model. The output of the model is a landslide peril map with 5 classes: very high, high, medium, low, and very low. It would be worth the managers and policymakers to use these results for protecting the susceptible areas.

Mass movements are a type of morphodynamical phenomena that are usu ally related to various factors and occur on slopes in mountainous areas. Every year, damages caused by landslides lead to financial loss and death over the world (Fathi et al., 2018). According to previous studies, landslides cause 17% of the world's natural disasters. Mortality rates from 1903 to 2004 for different continents, including Asia, United States, Europe, Africa, and Australia were 29%, 39%, 30%, 1%, and 1%, respectively (Kohorest et al., 2005). Over the last three decades, several research studies have been conducted on landslide susceptibility mapping using different methods for developing their classification. All mapping methods are classified into five different groups including landslide distribution analysis, qualitative, statistical, deterministic, and frequency analyses (Vanvestern, 2003). Iran is prone to landslide phenomena due to natural conditions such as mountain topography, high tectonic and seismicity activity, geological and climatic diversity. Environmental factors affecting the occurrence of landslides are slope degree, aspect, plan curvature, elevation, land use, lithology, distance from the road, river and fault, topographic moisture index, slope length index or sediment transport and vegetation cover (Silakhori et al., 1400).

Firstly, 134 points were identified using the Iranian landslide database and field survey in order to prepare a landslide susceptibility map using Bayesian theory. In the present study, 12 factors were used including elevation, slope and aspect, plan curvature, distance from the fault, road, and river, land use, geology, sediment transport index (STI), topographic wetness index (TWI), and vegetation cover. Then, topographic (1:50000), geology (1:100000), soil maps, and satellite imagery (Indian Remote Sensing) for 2012, were prepared and classified in ArcMap and ENVI environments. In order to evaluate Bayesian theory in landslide risk analysis, the relative performance curve of the relative efficiency of variables (ROC) was applied. This index is used to determine the accuracy and efficiency of the model (Egan, 1975; Williams et al., 1999). The area under the ROC curve represents the predicted value by describing its ability which accurately estimates the occurred events (landslide occurrence) and its non-occurred events (non-landslide occurrence). Therefore, the area under the curve is used as the model accuracy assessment. In the present study, 134 points of the landslide’s phenomena were used for modeling (70%) and accuracy assessment (30%) (Pourghasemi et al., 2013).

The results of the factors affecting the occurrence of landslides using Bayesian theory in the study area showed that most of the landslides occurred in the class of 15-30 degrees with a weight of 1.28, which is also reported by Eracanoglu and Gokceolu (2004). The reason is that human intervention on these slopes causes more susceptibility (Yalcin et al., 2011). The study of aspect shows that most of the landslides occurred in the west and south directions with a weight of 2.21 and 2.41, respectively, which is confirmed by the results of Shams and Alizadeh (2019). The results of plan curvature showed that most of the landslides happened with a weight of 1.1 in convex slopes, which is close to the results of Pourghasemi (2013). Convex slopes usually have the highest landslides which is also reported in previous studies (Vanvewsten et al., 2003; Jaaferi et al., 2014). The altitude of 500-1000m covered by sandstones, chile and siltstones showed a significant relationship with a high number of landslides (Ayalew & Yamagishi, 2005). Among different land uses, rangeland (weight of 3.48) indicated the most significant relationship in landslide occurrence, which is reported by Shams and Alizadeh (2019).  
The results of distance from roads, rivers, and faults showed that most landslides occurred at distances of more than 0-100 meters which confirms the results of previous studies (Pourghasemi & Mohammadi 2016). Also, the relationship between vegetation cover and landslides showed that the highest percentage occurred in class 0.5-0.3 with a weight of 3.56. In addition, the highest slippage for topographic wetness index and sediment transport index (river capacity) occurring were related to 6.39- 11.29 and 11.84-19.6 classes (weights of 1.009 and 1.17), respectively. The area under the curve was calculated at 85.56% in the model validation for the Bayesian model, which was classified as a very good performance. Therefore, the results of our study can play an important role in the management and planning of the Talar watershed.