LANDSLIDE is one of the natural HAZARD.which mainly occurs in mountainous areas. It gives sudden changes to the morphology of the ground and causes drastic damages to residential areas, roads, and farmlands, that is xhy governments and scientific and research centres have pahd special attention to the phenomenon of LANDSLIDE and some measures have been taken to identify and control them. In 1374, Abkar, a village of Farsan city Chahar–Mahal Bakhtiary province–was buried down with its50 inhabitants due to the LANDSLIDE (Ghobadi, M.H.,1381,P.164). In the area under study, specially in the northern ranges of cherkindag, which looks to Bazargan plain and New Maku, paleo and New LANDSLIDE can be seen. Fortunately, since the area is for from the residential area, in has not produced serious problems for the people. The aim of the present article is identifying the area with a LANDSLIDE potential. Therefore, using topographical maps and considering the methodspresented by Anbalagan, I would provide a zonation plan of the LANDSLIDE HAZARD.to let the planning and designing managers, provided with an exact understanding and identification of these area, could consider the geomorphological risks in using them.

Identification of Regions Having Potential for LANDSLIDE Occurrence is One of The Basic Measures in Natural Resources Management. Different LANDSLIDE HAZARD.Zonation Models are Proposed Based on The Environmental Condition and Goals. Nowadays in Countries Involved With LANDSLIDE Problem, There is an Increase Trend to Evaluation and Zonation of Risk and dDamage of This Phenomena. Existing LANDSLIDEs As Earth Evidence Was Identified and LANDSLIDE Inventory Map Was Provided. Factors Layers As Geology, Slope, Aspect, Distance From River, Istance From Road, Distance From Fault, Land Use, Rain and Elevation in Arc-GIS Software Was Provided. The LANDSLIDE HAZARD.Zonation Maps are Based on The Information Value, Certainty Factors and Multiple Regressinon Models in Arc-GIS Environment Provided. The Level of Similarity Potential HAZARD.Figures of These Models Were Compared With The LANDSLIDE Inventory Map in The SPSS Environments. Results of Research Showed That There are a Significant Correlation between the Potential HAZARD.Figures with the Area of LANDSLIDEs in Three Models. The Multiple Regression Model Have Hieghest Correlation in This Watershed So Multiple Regression Modl is The Best Model for Application in The Bagh Dasht Watershed.

LANDSLIDE mapping is one of the approaches (which can be) used to define the slope stable critical areas and the maps prepared based on this demarcating are used in desining sustainable development. The purpose of this study is to compare the LANDSLIDE susceptibility mapping models based on Fuzzy logic, Information value and area Density applied in the Chamsangar watershed. According to the tectonic and Iran old basins divisions, this basin is a part of folded Zagros. To study the stability of slopes in the Chamsang field, in first step the sliding point and subsequent distribution maps of LANDSLIDE were prepared using satellite images and field visits (recording the sliding points by GPS). in this study, LANDSLIDE-related factors such slope, aspect, altitude, lithology, rainfall, landuse and distance from the road and drainage used in the LANDSLIDE susceptibility analyses. To assess and classify was there models outputs the Sum of Quality (QS) index used. Results show that area Density model with QS=1. 85 is the more suitable than Information value model with QS=1. 60 and Fuzzy logic with QS=0. 554 based on the out put of the area Density model, the 36. 31, 44. 78, 16. 62, 1. 65, and 0. 63 percent of the area is located in very low, low, medium, high and very high risk classes, respectively.

In this research, evaluation of statistical methods in Predication of LANDSLIDE HAZARD.has been performed using data Processed by Geographic Information System (GIS). For this purpose, Rudbar area as an appropriate example for north part of the Iran was selected.Our investigation indicates that lithology distance from faults, vegetation cover, land use, rain fall, and maximum acceleration rate are the main LANDSLIDE controlling factors in the area. Each of these factors was used as a thematic layer for LANDSLIDE HAZARD.zonation mapping.Univariate and multivariate statistical analysis were used for LANDSLIDE HAZARD.analysis. Regression analysis indicates that classification of each parameter map into a number of relavant calsses such as slope, rainfall, . . . reduces accuracy of prediction. Also weighting of samples based on area of each unit and LANDSLIDE occurrence increase accurracy.In general, among univariate statistical analaysis, area density method represents better results. Multivariate analysis indicates appropriate results for continous data compared to discrete data. On the other hand weighting of samples based on values such as area of ground units or percentage of LANDSLIDE in each unite improves the results.

LANDSLIDEs are major natural HAZARD. which not only result in the loss of human life but also cause economic burden on the society. Therefore, it is essential to develop suitable models to evaluate the susceptibility of slope failures and their zonations. This paper scientifically assesses various methods of LANDSLIDE susceptibility zonation in GIS environment. A comparative study of Weights of Evidence (WOE), Analytical Hierarchy Process (AHP), Artificial Neural Network (ANN), and Generalized Linear Regression (GLR) procedures for LANDSLIDE susceptibility zonation is presented. Controlling factors such as lithology, landuse, slope angle, slope aspect, curvature, distance to fault, and distance to drainage were considered as explanatory variables. Data of 151 sample points of observed LANDSLIDEs in Mazandaran Province, Iran, were used to train and test the approaches. Small scale maps (1:1,000,000) were used in this study. The estimated accuracy ranges from 80 to 88 percent. It is then inferred that the application of WOE in rating maps’ categories and ANN to weight effective factors result in the maximum accuracy.

It is very important to recognize the areas with high potential risk of LANDSLIDE in order to resist that. In a case study, thirty three LANDSLIDEs have been observed in Safarood watershed. The purpose of this study is to recognize factors and zonation of potential areas for LANDSLIDE. This paper covers specific factors including lithology, fault, height, slope, direction, rainfall, soil, vegetation and villages of the area. GIS is used to prepare and integrate thematic layers, combine them by weight model and finally to prepare a map of LANDSLIDE HAZARD. AHP statistic method is used for weighting the criteria. The criterion for weighting the existing elements in each layer has been based on the most important role played in the layer and in LANDSLIDE. after combining various layers in GIS medium, the map of LANDSLIDE HAZARD.zonation is obtained in four zonation with every high HAZARD. high HAZARD. medium and low HAZARD. It has been determined that approximately 31% of the surface of the area is encountered very high and high HAZARD.LANDSLIDEs. 94% of the cases observed in LANDSLIDE, based on the mentioned zonation and the model, are in the zonation of very high and high HAZARD. that show the high accuracy of this method.

Forest road networks are essential structures to achieve the forestry aims, but these structures include most implemental and environmental costs. To minimize these costs, the effective factors have to be considered in forest road design which LANDSLIDE susceptibility is one of the major factors. Road construction on susceptible terrain increase road maintenance cost and environmental impacts. LANDSLIDE HAZARD.zonation is a method that can be applied to recognize and avoid the unstable terrain. In this research, the LANDSLIDE HAZARD.zonation map of Baharbon district in Kheyroud Research Forest was developed using a combined model, AHP and Density Area methods. First, LANDSLIDEs distribution map of study area was prepared in ArcGIS using field observations and then the map of effective factors in LANDSLIDE occurrence were prepared. To determine the weight of effective factors AHP was used. Afterward a bivariate statistical model, Density Area, was used for calculating the weight of classes of effective factors maps. By combining the effective factors maps and classifying the combined map with regard to breakpoints of frequency curve, the LANDSLIDE HAZARD.zonation map was developed. According to the results, 14.7%, 26.9%, 38.9%, 15% and 4.5% of the district were classified as very low, low, moderate, high and very high HAZARD. respectively. Finally due to lack of LANDSLIDE occurrence in slope class of 0-15%, as well as it is suitable slope for road design, this class was suggested as positive control points. The other slope classes that were in low and very low HAZARD.were defined as second priority for road design. Road building in high and very high HAZARD.is possible, if increasing the number of culverts, also biological and mechanical reinforcements of cut and fill slopes.

This article aims to evaluate sensitivity of Goijabel basin lands in landsliding based on GIS and AHP. The main reason for selection of this basin for research is to observe the current landsliding in the region by the author and environmental preservation attitude of the authors. This basin is one of the sub-basins of Aharchi in area of 7406 Hectares with geographical statues of 46, 47, 21.26 to 46, 56, 53.64 east longitude and 38, 21, 42.13 to 38, 27, 39.04 north latitude. The criteria selected for analysis of landsliding sensitivity in the study basin involve nine parameters (slope, aspect, height, lithology, fault, road, landuse, precipitation and main water ways) extracted from basic maps, field studies and spot satellite images and their primary valuation was conducted by using GIS techniques in five layers relative to the effect of each measure on incidence of landsliding. Then pair comparison of the layers was carried out by using Mariyoni attached program (AHP extension) and final map of the land sensitivity in landsliding was extracted. The results of landsliding sensitivity analysis in Goijabel basin showed that the most effective factor in land sliding is lithology with weight of 0.3113 according to the highest weight and then the height with minimum weight of 0.0178 is the criterion with lowest effect in landsliding. Also the analysis of landsliding danger in the study basin shows that endangered lands with high sliding capability involve 1222 ha which cover %16.5 of the total regions.

There are several methods for zonation of LANDSLIDE HAZARD. They can be generally divided into two groups; direct and indirect methods. In this study, we used Artificial Neural Network (ANN) as an indirect method for LANDSLIDE HAZARD.zonation in Semnan province. A total of 49 LANDSLIDEs or slide zones, that overlaid on the topographical map with the scale of 1:50000, were studied. Maps of factors such as lithology, slope, aspect, land use, buffer of faults, DEM, precipitation were then prepared. The sedata were then normalized, according to the maximum value of each factor. The normalized data was then fed into a multi-layer preceptor with back error propagation algorithm. The network had 3 layers, first layer as input layer, had 7 input elements each of which related to one factor. The second layer as hidden layer had 20 process element. The last layer as output layer consisted of one process elements which have been trained to offer5 level of HAZARD.risk. This structure was found as the best optimized structure through extensive simulations. Our data base consisted of 2016 records. This database was randomly divided into the two separate groups. One as train database consisted of 1626 records which used to train the established ANN. The second group named the testing database consisted of 400 records, which used to test the performance of the ANN. The accuracy of network for predicting LANDSLIDE HAZARD.was measured about 91.25%. Results reveal that artificial neural network model the LANDSLIDE HAZARD.zonation better that other approaches.