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.

Golestan province is always exposed to LANDSLIDE HAZARD because of climatic and physiographic conditions. This paper presented a data integration framework based on the Dempster-Shafer theory of evidence for LANDSLIDE susceptibility mapping with multiple geospatial data. For this purpose, the first LANDSLIDEs inventory map has been created by LANDSLIDE points of LANDSLIDE Data base of Iran (392 LANDSLIDE points). Then, effective parameters on LANDSLIDE event such as slope, aspect, altitude, slope curvature, geology, land use, distance of drainage, distance of road, distance of fault, stream power index, sediment transport index, and the zone of rainfall were prepared as a layer in GIS environment. Using of Dempster-Shafer theory weight of each factor has been calculated. These weights were inputted in the effective factor maps and for each factor will be created a weight map. The sum of the weighted layers was used to generate the LANDSLIDE HAZARD map. The efficiency of Dempster-Shafer theory in LANDSLIDE HAZARD susceptibility mapping was evaluated using ROC and area under curve method. The results indicate that the Dempster-Shafer theory has enough accuracy for producing the LANDSLIDE HAZARD zones in this case study.

The mentioned model is one of the statistical ANALYSIS methods for LANDSLIDE HAZARD predication based on quantitatively defined weight values. The method used is called LANDSLIDE index method. A weight – value for a parameter class, such as a certain lithological unit or a certain slope class, is defined as the natural logarithm of the LANDSLIDE density in the class divided by the LANDSLIDE density in the entire map. The method is based on map crossing of a LANDSLIDE map with a certain parameter map, in geographical information system. The mentioned model is run in Amiry and Haftan subbasines. The output results has assessed base on Density ratio (Dr) index. Comparisons of the results with those of other researchers confirm the modeling efficiency.

LANDSLIDEs in the hilly terrain of Sefidargoleh area in Semnan province cause many damages to residential area, roads, ...every year. To address this problem, a statistical method called multiple logistic regressions has been used to create a LANDSLIDE HAZARD map for study area. Data included digitized geology, slope, aspect, DEM, landuse, precipitation and buffer of faults manipulated using SPANS in GIS environment. Logistic regression relates predictor variables to the occurrence or nonoccurrence of LANDSLIDEs within geographic cells. This information was used to produce a map showing the probability of future LANDSLIDEs. Results indicated that slope and litology are the most important variable for estimating LANDSLIDEs. Precipitation and buffer of faults were excluded from the final ANALYSIS because these variables did not add the predictive power of the logistic regression.      

Among various regions of Iran, Northern areas are taken in account as a vast area having the most LANDSLIDEs. Due to population as well as natural situations is ready to awful fanatical and vital damages. Examining effective factor on occurrence of locally earthquakes and also zonation on lands of a region can be an effective measure for minimizing related damages for instant by moving population from the risky area. Regarding to this measure, zoning map should be prepared for those regions. The basic method applied through the present study includes field examining and preparing data layers based on existing maps and other sources, the effective factors in occurrence of earthquakes were examined and the most effective factors were determined as geology, soil examination, linear elements, slope, rainfall, land use and elevation. Results showed that the significance rate for geology, soil examination, linear elements, slope, rainfall, land application, and elevation are 0/4, 0/22, 0/16, 0/11, 0/045, and 0/045, and 0/002, respectively (defined scale between 0 to 10). Thus it can be demonstrated that effect of geology on the region was more than other factors; soil examination, linear factor, slope, rainfall, land use and elevation, respectively.

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.

LANDSLIDEs are major natural HAZARDs 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 HAZARDs that show the high accuracy of this method.