Landslide is considered one of the greatest natural hazards that causes high damage direct or indirect financial every year. One of the areas prone to natural factors in the occurrence of this watershed and from the North Country. One of the occurs Susceptible areas in watershed vaz. Landslide hazard zonation map was prepared by using AHP and 6 certainly factor, landslide index, weight factor, frequency ratio, combination weight factor with landslide index and combination landslide index with AHP. The validate of models showed combination landslide index with weight factor model the value 1.02 was best performance in predicting risk of landslide. Lithology and distance from the road were the most important factors in landslide occurrence. Most statistical models to predict the risk of landslide showed 70 percent of the classes area of landslide risk is related to high risks and very high.

Identification of areas susceptible of landslide is one of the primary measures in managing and reducing the damage caused by such phenomena. In this regard, according to the goal and environmental conditions, different zoning models have been proposed. In order to zone and study the areas susceptible of landslides in the north of Neyshaboor, AHP method was applied. Thus, factors causing landslides including slope, lithology, land use, rainfall, the distance from river, the distance from the fault and the distance from the road were analyzed as the main factors for the occurrence of landslide. Layers of information related to each of the factors effective in the occurrence of landslides were prepared by the means of GIS analysis, and effective factors were evaluated using the AHP and paired comparisons. Finally the landslide hazard zonation map was prepared. Considering the final weight of each of the factors, it was concluded that the slope of the landslide and lithology in this area are the first and second most important factors influencing the landslide degree. Also the results of this zoning and the existing distribution of landslides confirmed that places susceptible of landslide in this region are based on landslide areas.

The Landslide is one of the natural hazards that causes a lot of mortal and financial losses in high-altitude and seismic mountainous areas annually. Considering the importance of roads as a pillar of sustainable forest management in this study, we have tried to estimate the sensitivity of landslide occurrence in 8 forestry series with using frequency ratio approach. Initially, the slip position in the series occurred with ground scrolling by GPS, then the effective factors in the occurrence of landslides were determined for the study area. Raster maps of the effective factors were prepared and classified. Considering the distribution of landslides recorded in the field and the categories of effective factors, the frequency ratio for each class of each factor was calculated and their relationship with the occurrence of landslide was investigated. The results of the relationship between positioning and slipping showed that the highest amount of landslides occurred on the lower side of the road, and by increasing the age of the roads, the amount of landslides is reduced. The relationship between soil and slip occurred on the class with the code 2.2.3, which represents the soil texture of loamy, loamy clay and clay that the highest number of slides was observed. Elevation class 300 to 600 meter has the highest number of landslides. The relationship between the slope and slip showed that with increasing the slope up to 40%, the number of landslides was increased, but then the trend was decreasing. The relationship between geological and landslide showed that the highest number of landslides was observed in the class with L-PLL2 ​​code representing relatively high peaks with moderate slope, average soil depth, stability and permeability. The relationship between rainfall and landslide factors showed that the greatest slip is occurring between 600 and 800 mm. In the northern, northeastern and northwest directions, the highest slip occurred. The evaluation results of the model with the ROC method indicated that the area under curve (AUC) was 0.688 (68.8%).

IntroductionIranian territory has the main prerequisites for the occurrence of a wide range of landslides due to its mountainous topography, tectonic activities, high seismicity, and different geological and climatic conditions. Therefore, reducing the effects of natural disasters, particularly landslides, is one of the key challenges for land-use planners and policymakers in this field. In this study, the southern side of the Ahar Chai basin from Nasirabad Village to Sattarkhan Dam is evaluated for the probability of the landslide occurrence. This region is highly susceptible to landslide occurrence because of the extensive manipulation and its natural conditions. Indeed, the occurrence of the large shallow landslides in this region is an indication of this susceptibility. In this study, Linear Regression Model has been used to prepare the landslide zonation.
MethodologyThe study area was the southern sides of the Ahar Chai River, from Nasirabad village in Varzaghan to the Sattarkhan Dam, with an area of 128 km2. In order to study the potential of the landslide occurrence in this region, nine main factors including slope, slope direction, lithology, land use, precipitation, distance from the fault, distance from the river, distance from the road, and vegetation were identified. The model which was used in this study was Logistic Regression. This model is one of the predictive statistical methods for dependent variables in which zero and one respectively indicate the occurrence and non-occurrence of landslides. In addition, instead of being linear, the regression of the variables is S-shaped or logistic curve and the estimations are in the range of zero-one. Indeed, values close to zero indicate the low probability of the occurrence and values close to one indicate the high probability of the occurrence.
DiscussionIn Logistic Regression model, after entering the data into the Logistic Regression model and using the effective parameters in Idrisi software, the coefficients of the model were extracted. A value of 965, which represents a very high correlation between the independent and dependent variables, was obtained for the ROC index. After determining the validity of the Logistic Regression model, using the above indicators, landslide sensitivity zonation map was prepared. In the present model, the land use factor with the highest coefficient was the best predictive variable in determining the probability of the landslide occurrence in this region. In addition, the SPI index and the distance from the fault had respectively the second and third highest coefficients. After zoning the landslide, the slip area was calculated for each class and its results showed that zones with highest risk had the lowest area percentage and these areas were located in the western slopes.
ConclusionThe results showed that while land use, lithology factors, and SPI index with positive coefficients had higher correlation, the other factors with negative coefficients had lower correlation. Based on the map, the western, southern, and the north-eastern parts of the region have the highest potential for landslide occurrence. Furthermore, the high value of the ROC index and its proximity to number one indicates that landslides in the study area have a strong correlation with the probability values derived from the Logistic Regression Model. In addition, the assessment of the SCAI scaling hazard zonation map shows that there is a high correlation between the hazard map with the existing slip points and the field observations of the area. It can be said that, in addition to the natural factors, some human factors including unstructured road construction may play an important role in the occurrence of the landslides. It is also necessary to avoid making changes in the ecosystems and land use. Finally, any policies to construct structures should be commensurate with the geomorphologic and geological conditions.

Natural disasters management requires local information in order to be ready against dangers and reduce their procedure. Hence، evaluation of landslide occurrence in the area which is prone to landslide due to geographical condition and human constructions is high crucial. Ghale Chai dam located at Ajabshir Watershed is one kind of such areas. So the aim of present investigation is to identify hillside instabilities and movements and their influencing factors to prevent their harmful effects on natural resources and other parts of economical and engineering development and recognize points with high prone of danger. Hence، the aim of present investigation is to assess analytical network process and Heuristic method in determining the landslide prone areas in range axis and reservoir of Ghale Chai dam of Ajabshir. Methods and Materials: The efficiency of network analysis process and logistic regression method were studied to investigate landslide potential in studied area dam. ANP model building requires the definition of elements and their assignment to clusters and a definition of their relationships (I. e. the connections between them indicating the flow of influence between the elements). Like AHP، ANP is founded on ratio scale measurements and pair wise comparisons of elements to divide priorities of selected alternatives. In addition relations among criteria and sub-criteria are included in evaluations، allowing dependencies both within a cluster (inner dependence) and between clusters (outer dependence) (Saaty: 2001). Pairwise comparison is now done، both for weighting clusters (criteria) and for estimating the direction and importance of influences between elements، numerically pictured as ratio scale in a so-called super matrix. Network analysis process was used for the first time in Iran in order to evaluate landslide، done using super decision and arc GIS software. However، to assess landslide susceptibility using heuristic method there are two common approaches: direct and indirect method. The first method applies direct assessment to interpret susceptibility in the field on the basis of detailed maps (geomorphological maps، for instance). The latter does not assess directly in the field، but via data integration techniques in any particular software. This study uses indirect heuristic method. Heuristic approach is a semi-qualitative method. Besides uses knowledge properties (expert opinions، previous research results or literature recommendations)، it also uses index-based procedures such as simple ranking and rating or analytical hierarchy process (AHP) in assigning weight and creating model. Concerning this، scoring and weighting process are crucial to build a model in heuristic approach.

Considering research questions، a three-layer network model composed of target layer، criteria layer and options layer was designed and organized in network analysis process. The priority of danger classes was determined based on their coefficients after doing paired comparisons among elements and clusters. Zoning map was classified in five classes from very high to very low. The weighting judgment process in pair-wise comparison gives a weight for every Influencing factor. From the calculation، the final criteria tree (with weight in 2 digits) was created. Bigger weights indicates that، the pertinent factor gives bigger influence toward the model. Aspect has the biggest contribution (0. 2519)، followed by distance to road and litology with value 0. 1786 and 0. 1747، respectively. On the other side، the lowest contribution is given by slope (0. 0387)، followed by Dem (0. 0590). No negative weights in heuristic method. The inconsistency value is 0، 0 62194: smaller than 0، 1. It means، according to SMCE validation، the choosing process is consistent. No improper stage while positioning the factor based on its importance to another After running paired comparisons between elements and clusters the priority of the danger classes based on their significance was determined and the coefficients of the factors showed that the aspect factor has the maximum effect in occurrence of the landslides if the area.

Considering the important role of landslide in watersheds, the present study has investigated the effective factors in landslide occurrence in Ardal watershed, Chaharmahal Bakhtiari province. In this research, landslide inventory map was prepared using satellite images, aerial photos and field investigation. At the same time, maps of the effective factors in the landslide event were prepared. Slope, direction of gradient, height, lithology, rainfall, distance from fault, river density, distance from road and land use were selected as factors affecting landslide occurrence. Logistic regression method has been used in GIS environment to prepare landslide sensitivity map. Using these factors and land inventory map, landslide susceptibility and the significance of each selected factor for landslide occurrence in the area, were identified by logistic regression method. The results showed that "distance from the river", "land use" and lithology are considered as the most important factors influencing the landslide occurrence in the region. .