Landslide Susceptibility Analysis Using GIS and Logistic Regression Model A Case Study In Malang, Indonesia

Abstract

Landslide susceptibility mapping is one of the most important counter measures in landslide risk reduction, as this paper will show. A method for determining landslide-prone areas by combining multivariate statistical analysis and GIS was demonstrated, with Malang, Indonesia, as the study area. Seven spatial parameters – elevation, slope, aspect, flow accumulation, land use/land cover, geology and soil – were used in the analysis. Three of these parameters were identified as being more likely to cause landslides. These particular parameters were used to produce a landslide susceptibility map, divided into five classes. Gain statistics were then applied to assess the accuracy of the model; 77% accuracy was the result. The output was overlaid with a land use/land cover dataset to investigate which areas were prone to landslides. The result showed that in the study area, forest and upland food crops are most vulnerable to landslide, followed by mixed tree crops and settlements.