ALIREZA Arabameri; kourosh shirani; Mahdi Tazeh
Abstract
Present study seeks to identify effective factors in landslide occurrence and landslide sensitivity zonation using logistic regression and multivariate linear regression. Accordingly, through the interpretation of arial photos with scale of 1:40000, geological, topographic maps, and field survey using ...
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Present study seeks to identify effective factors in landslide occurrence and landslide sensitivity zonation using logistic regression and multivariate linear regression. Accordingly, through the interpretation of arial photos with scale of 1:40000, geological, topographic maps, and field survey using GPS, landslide hazard map was prepared as dependent variables. For determination of effective factors in landslide occurrence, using Support Vector Machines in Rapid Miner Software, the numerical values of the parameters were analyzed and from 21 selective data layers, 15 data layers were selected and were prepared and digitized for zonation map as the independent variable in ArcGIS 10.1. After weighing the layers, zonation map was prepared using selective method in five classes: very low, low, moderate, high and very high. Result of weighting layers showed that in both methods, land use and aspect have the greatest impact on landslides. The ROC (Receiver operating characteristic) curves and area under the curves (AUC) for landslide susceptibility maps were constructed and the areas under curves was assessed for validation purpose and its values showed that multivariate linear regression model (0.890) has a higher efficiency than the logistic model (0.829) for landslide hazard zonation. According to result of superior model (multivariate linear regression), 16046.1 hectare (20.13%) of the region was found to be located in high risk class and 15671.2 hectare (19.66%) was in very high risk class.
