Landslides

, Volume 7, Issue 1, pp 13–30

Regional landslide susceptibility analysis using back-propagation neural network model at Cameron Highland, Malaysia

Authors

    • Institute of Cartography, Faculty of Forestry, Geo and Hydro-ScienceDresden University of Technology
  • Saro Lee
    • Geoscience Information CenterKorea Institute of Geoscience and Mineral Resources (KIGAM)
Original Paper

DOI: 10.1007/s10346-009-0183-2

Cite this article as:
Pradhan, B. & Lee, S. Landslides (2010) 7: 13. doi:10.1007/s10346-009-0183-2

Abstract

This paper presents landslide susceptibility analysis around the Cameron Highlands area, Malaysia using a geographic information system (GIS) and remote sensing techniques. 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. Ten landslide occurrence factors were selected as: topographic slope, topographic aspect, topographic curvature and distance from drainage, lithology and distance from lineament, soil type, rainfall, land cover from SPOT 5 satellite images, and the vegetation index value from SPOT 5 satellite image. These factors were analyzed using an advanced artificial neural network model to generate the landslide susceptibility map. Each factor’s weight was determined by the back-propagation training method. Then, the landslide susceptibility indices were calculated using the trained back-propagation weights, and finally, the landslide susceptibility map was generated using GIS tools. The results of the neural network model suggest that the effect of topographic slope has the highest weight value (0.205) which has more than two times among the other factors, followed by the distance from drainage (0.141) and then lithology (0.117). Landslide locations were used to validate the results of the landslide susceptibility map, and the verification results showed 83% accuracy. The validation results showed sufficient agreement between the computed susceptibility map and the existing data on landslide areas.

Keywords

Artificial neural networkLandslide susceptibilityGISMalaysiaRemote sensingBack propagationValidation

Copyright information

© Springer-Verlag 2009