Abstract
In this paper, a GIS-based methodology has been used to produce a landslide susceptibility map. The area selected is along the E-W highway in Malaysia where frequent landslides occur. The susceptibility mapping was based on a multivariate statistical method namely the logistic regression. The spatial database for factors that influence landslide occurrence were prepared from different sources including topographical maps, geological maps, satellite data, hydrological data, soil data and field data. Ten prepared thematic maps of factors were: slope gradient, slope aspect, elevation, curvature, and distance from road, drainage density, lithology, lineament density, soil, and rainfall. All maps were subdivided into different classes by its value or feature and then were converted to raster format in the ArcGIS 9.3, each representing an independent layer of causative factor in the constructed spatial database. the contribution of each factor towards landslide susceptibility was evaluated using the logistic regression model .The Wald test in logistic regression analysis suggests that slope gradient, lineament density, rainfall, distance from road and lithology play a positive important role in the landslide susceptibility. However, the curvature and drainage density factors play a negative important role in the landslide susceptibility in the study area. The results of the analysis have been validated by calculating the AUC of the prediction rate curve which shows an accuracy of 80.97%, indicating a high quality susceptibility map obtained from the logistic regression model. The map could be used by decision makers as basic information for slope management and land use planning