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

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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.

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Acknowledgments

The first author would like to thank Alexander von Humboldt Foundation (AvH), Germany for awarding a visiting scientist position to carry out research at Dresden University of Technology, Germany. The authors would also like to thank Filippo Catani and three anonymous reviewers for their careful review of the original manuscript and their valuable comments. The authors would like to thank the Malaysian Remote Sensing Agency for providing various datasets used in this analysis.

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  1. Institute of Cartography, Faculty of Forestry, Geo and Hydro-Science, Dresden University of Technology, 01062, Dresden, Germany

    Biswajeet Pradhan

  2. Geoscience Information Center, Korea Institute of Geoscience and Mineral Resources (KIGAM), 30, Kajung-Dong, Yusung-Gu, Taejon, Korea

    Saro Lee

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Correspondence to Biswajeet Pradhan.

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Pradhan, B., Lee, S. Regional landslide susceptibility analysis using back-propagation neural network model at Cameron Highland, Malaysia. Landslides 7, 13–30 (2010). https://doi.org/10.1007/s10346-009-0183-2

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