Earthquake-induced landslide-susceptibility mapping using an artificial neural network

Abstract

The purpose of this study was to apply and verify landslide-susceptibility analysis techniques using an artificial neural network and a Geographic Information System (GIS) applied to Baguio City, Philippines. The 16 July 1990 earthquake-induced landslides were studied. Landslide locations were identified from interpretation of aerial photographs and field survey, and a spatial database was constructed from topographic maps, geology, land cover and terrain mapping units. Factors that influence landslide occurrence, such as slope, aspect, curvature and distance from drainage were calculated from the topographic database. Lithology and distance from faults were derived from the geology database. Land cover was identified from the topographic database. Terrain map units were interpreted from aerial photographs. These factors were used with an artificial neural network to analyze landslide susceptibility. Each factor weight was determined by a back-propagation exercise. Landslide-susceptibility indices were calculated using the back-propagation weights, and susceptibility maps were constructed from GIS data. The susceptibility map was compared with known landslide locations and verified. The demonstrated prediction accuracy was 93.20%.