Pollution Research Paper

Vol.30, Issue 3, 2011; Page No.(427-433)




Understanding where the landslides are most likely to occur is crucial in reducing property damage and loss of life in future landslides. In this research logistic regression analysis is used because less statistical assumption compared with other multiple statistical models and creating the best relationship between presence and absence of landslides and asset of causative factors to supply landslide susceptibility map in syahdare basin at first based on field surveys, local interview and review of previous works in similar region, ten primary causative factors on landslide occurrence such as elevation, slop gradient, aspect, rainfall, distance from fault, distance from drainage, distance from road, land use and lithology in study area recognized and their information layers has been created in GIS by using ARC GIS 9.2 soft wares based on photograph interpretation and field surveys 75 landslides were recognized and also another 75 non landslides were selected randomly all over the basin after overlaying all points(landslides and nonlandslides) with causative factors layers 1 and 0 codes were belonged to presence and absence of landslides respectively. After entering independents variables including all coded classes and dependent variables including 150 landslides and non-landslides in to SPSS 12 and selecting forward stepwise method, data analysis were performed interpretation of coefficients obtained of logistic regression function analysis indicates that aspect and lithology, being miscorrelated with landslide occurrence by more than 0.05 significance are deleted from the model. At last, statistical model was performed based on the most effective factors on landslide occurrence including slope, elevation, rainfall, distance from drainage, distance from fault, land use and distance from road respectively. After transmitting this model to ARC GIS 9.2 soft ware, landslide susceptibility map of syahdare basin was performed whit four classes. Therefore 51.94 residual area is located in high hazard regions. Model and then susceptibility map Verity was then susceptibility map Verity was assessed using -2LL, and Snell R2, Nagelkerk R2, occurrence ratio comparison and considering the deference percentage between landslide observed density and predicted probability and it was reliable.

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