Comparison and Validation of Landslide Susceptibility Maps Using an Artificial Neural Network Model for Three Test Areas in Malaysia

Research Article| May 01, 2010 Comparison and Validation of Landslide Susceptibility Maps Using an Artificial Neural Network Model for Three Test Areas in Malaysia BISWAJEET PRADHAN; BISWAJEET PRADHAN 1Institute for Cartography, Faculty of Forestry, Geo-, and Hydro-Science, Dresden University of Technology01062 Dresden Germany 1. Corresponding author: email: biswajeet.pradhan@mailbox.tu.dresden.de; phone: +49-35146333099. Search for other works by this author on: GSW Google Scholar MANFRED F BUCHROITHNER MANFRED F BUCHROITHNER 1Institute for Cartography, Faculty of Forestry, Geo-, and Hydro-Science, Dresden University of Technology01062 Dresden Germany Search for other works by this author on: GSW Google Scholar Author and Article Information BISWAJEET PRADHAN 1. Corresponding author: email: biswajeet.pradhan@mailbox.tu.dresden.de; phone: +49-35146333099. 1Institute for Cartography, Faculty of Forestry, Geo-, and Hydro-Science, Dresden University of Technology01062 Dresden Germany MANFRED F BUCHROITHNER 1Institute for Cartography, Faculty of Forestry, Geo-, and Hydro-Science, Dresden University of Technology01062 Dresden Germany Publisher: Association of Environmental & Engineering Geologists First Online: 02 Mar 2017 Online ISSN: 1558-9161 Print ISSN: 1078-7275 Copyright © 2010 EEGS Environmental & Engineering Geoscience (2010) 16 (2): 107–126. https://doi.org/10.2113/gseegeosci.16.2.107 Article history First Online: 02 Mar 2017 Cite View This Citation Add to Citation Manager Share Icon Share Facebook Twitter LinkedIn Email Permissions Search Site Citation BISWAJEET PRADHAN, MANFRED F BUCHROITHNER; Comparison and Validation of Landslide Susceptibility Maps Using an Artificial Neural Network Model for Three Test Areas in Malaysia. Environmental & Engineering Geoscience 2010;; 16 (2): 107–126. doi: https://doi.org/10.2113/gseegeosci.16.2.107 Download citation file: Ris (Zotero) Refmanager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentBy SocietyEnvironmental & Engineering Geoscience Search Advanced Search Abstract Landslides are common natural hazards in Malaysia. These landslides can be systematically assessed and mapped through traditional mapping frameworks using geoinformation technologies (GIT). The aim of this study was to apply, verify, and compare an artificial neural network model and its cross application of weights for landslide susceptibility analysis in three Malaysian study areas, namely, Penang Island, Cameron Highland, and Selangor, using a geographical information system (GIS). Landslide locations were identified in the study areas from interpretation of aerial photographs, field surveys, and inventory reports. The landslide-related spatial database was constructed from topographic, soil, geologic, and land-cover maps. The 11 factors that influence landslide occurrence were extracted from the database, and the weight of each factor was computed. Different training sites were selected randomly to train the neural network, and nine sets of landslide susceptibility maps were prepared. Landslide susceptibility maps were drawn for the study areas using weight derived not only from the data for that area, but also using that of each of the other two areas (nine maps in all) as a cross-check of method validity. The verification results show that among the nine cases, the best accuracy (83.99 percent) was obtained in the case of the Cameron-based Cameron weight, whereas the Penang-based Cameron weight showed the worst accuracy (70.58 percent). You do not have access to this content, please speak to your institutional administrator if you feel you should have access.