International Journal of Computer Science Issues

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This paper presents landslide susceptibility analysis in central Zab basin in the southwest mountainsides of West-Azerbaijan province in Iran using remotely sensed data and Geographic Information System. Landslide database was generated using satellite imagery and aerial photographs accompanied by field investigations using Differential Global Positioning System to generate a landslide inventory map. Digital elevation model (DEM) was first constructed using GIS software. Nine landslide inducing factors were used for landslide vulnerability analysis: slope, slope aspect, distance to road, distance to drainage network, distance to fault, land use, Precipitation, Elevation, and geological factors. This study demonstrates the synergistic use of medium resolution, multitemporal Satellite pour lObservation de la Terre (SPOT), for prepare of landslide-inventory map and Landsat ETM+ for prepare of Land use. The post-classification comparison method using the Maximum Likelihood classifier with SPOT images was able to detect approximately 70% of landslides. Frequency ratio of each factor was computed using the above thematic factors with past landslide locations. It employs the landslide events as dependant variable and data layers as independent variable, and makes use of the correlation between these two factors in landslide zonation. Given the employed model and the variables, signification tests were implemented on each independent variable, and the degree of fitness of zonation map was estimated Landslide susceptibility map was produced using raster analysis. The landslide susceptibility map was classified into four classes: low, moderate, high and very high. The model is validated using the Relative landslide density index (R-index method). The final, landslide low hazard susceptibility map was drawn using frequency ratio. As a result, showed that the identified landslides were located in the class (51.37%), moderate (29.35%), high (11.10%) and very high (8.18%) in Susceptibility zones.