assessment of slopes instability base on weights evidence using gis case study in “merek” area kermanshah, iran

â â â  the study area is located in the wes of iran, the southwest part of “bisetoon” mountain, and kermanshah city. it covers an area of ~1489 km2 . landslide have been the most common natural phenomenon in the area, which usually is triggered by heavy rainfall and seismic activities. although there are few active landslides in the area, but they are serious threats for population and infrastructures . the aim of this study is indicate the main factors influencing the slope instabilities. binary weights-of-evidence (wofe) offers a rigorous and objective statist i cal approach with many applications in modeling the real world conditions. a major advantage of wofe is the unbiased, statistically derived weight, which it provides for individual layers of data. however, wofe is perceived by many users as both an oversimplification because of its typically binary input, and yet overly complex in mathematics. multi-class wofe offers better representation of data distributions, but statistical noise can sometimes limit the effective use of multi-class weights. the weights -of- evidence are applied to evaluate the landslide susceptibility using gis. the relations between landslide distributions with the physical parameters such as lithology, elevation, slope gradient, lineaments and distance from streams, vegetation, and land use were analyzed by bayesian statistical model. the weights of evidence are applied to calculate each factor’s weight for the” merek” region in west iran, with numerous landslides. some factors (data layers) used for the preparation of the landslide susceptibility map are obtained from different sources, such as topographic maps, geological maps and satellite images. all the above data layers were converted to raster format in gis, each representing an independent variable of a constructed spatial database. the results of the analysis of mapping were validated using previous and recent landslide location. we found that six parameters namely, distance from stream and faults, slope, land use, profile curvature, plan curvature and lithology show better correlation with landslide susceptibility. in the study area of the present work, the methodology was tested by the means of consistent landslides found in hillslopes of nesar kooh valley. based on this study, six appropriate factors were selected for landslides potential hazard mapping in the merek area. results of six selected factors are in a good accordance with recently occurred landslides. . since most of the information related to landslides susceptibility map is geo-spatial, gis is capable to store, update, display, process, analyze and integrate the different geo-spatialâ dataâ