Approaches for delineating landslide hazard areas using different training sites in an advanced artificial neural network model |
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Authors: | Biswajeet Pradhan Ahmed M Youssef Renuganth Varathrajoo |
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Institution: | (1) Faculty of Forestry, Geo and Hydro-Science, Institute of Cartography, Dresden University of Technology, 01062 Dresden, Germany;(2) Geoscience Information Center, Korea Institute of Geoscience and Mineral Resources (KIGAM), 30, Kajung-Dong, Yusung-Gu, Daejon, Korea |
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Abstract: | The current paper presents landslide hazard analysis around the Cameron area, Malaysia, using advanced artificial neural networks
with the help of Geographic Information System (GIS) and remote sensing techniques. Landslide locations were determined in
the study area by interpretation of aerial photographs and from field investigations. Topographical and geological data as
well as satellite images were collected, processed, and constructed into a spatial database using GIS and image processing.
Ten factors were selected for landslide hazard including: 1) factors related to topography as slope, aspect, and curvature;
2) factors related to geology as lithology and distance from lineament; 3) factors related to drainage as distance from drainage;
and 4) factors extracted from TM satellite images as land cover and the vegetation index value. An advanced artificial neural
network model has been used to analyze these factors in order to establish the landslide hazard map. The back-propagation
training method has been used for the selection of the five different random training sites in order to calculate the factor’s
weight and then the landslide hazard indices were computed for each of the five hazard maps. Finally, the landslide hazard
maps (five cases) were prepared using GIS tools. Results of the landslides hazard maps have been verified using landslide
test locations that were not used during the training phase of the neural network. Our findings of verification results show
an accuracy of 69%, 75%, 70%, 83% and 86% for training sites 1, 2, 3, 4 and 5 respectively. GIS data was used to efficiently
analyze the large volume of data, and the artificial neural network proved to be an effective tool for landslide hazard analysis.
The verification results showed sufficient agreement between the presumptive hazard map and the existing data on landslide
areas. |
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Keywords: | artificial neural network landslide hazard GIS Malaysia |
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