Application of a Data Mining Model for Landslide Hazard Mapping

This paper deals with landslide hazard and risk analysis using Geographic Information System (GIS) and remote sensing data for Cameron Highland, Malaysia. Landslide locations were identified in the study area from interpretation of aerial photographs and field surveys. Topographical/geological data and satellite images were collected and processed using GIS and image processing tools. There are ten landslide inducing parameters which are considered for the landslide hazards. These parameters are topographic slope, aspect, curvature and distance from drainage, all derived from the topographic database; geology and distance from lineament, derived from the geologic database; landuse from Landsat satellite images; soil from the soil database; precipitation amount, derived from the rainfall database; and the vegetation index value from SPOT satellite images. 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 backpropagation 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 83.45% accuracy. The verification results showed sufficient agreement between the presumptive hazard map and the existing data on landslide areas.