Utilization of Optical Remote Sensing Data and GIS Tools for Regional Landslide Hazard Analysis Using an Artificial Neural Network Model

The aim of this study is to evaluate landslide hazard analysis at Selangor area, Malaysia using optical remote sensing data and a Geographic Information System (GIS). Landslide locations were identified in the study area from interpretation of aerial photographs and field surveys. Topographical, geological data and satellite images were collected, processed and constructed into a spatial database using GIS and image processing. A total of 10 landslide occurrence factors that were selected including topographic slope, topographic aspect, topographic curvature and distance from drainage; lithology and distance from lineament; land cover from TM satellite images; the vegetation index value from Landsat satellite images; precipitation data. These factors were analyzed using an advanced artificial neural network model to generate the landslide hazard map. Each factor's weight was determined by the back-propagation training method. Then the landslide hazard indices were calculated using the trained back-propagation weights, and finally the landslide hazard map was generated using GIS tools. Landslide locations were used to verify results of the landslide hazard map and the verification results showed 82.92% accuracy. The verification results showed sufficient agreement between the presumptive hazard map and the existing data on landslide areas.