HR: 0800h
AN: H41B-0296    [Abstracts]
TI: Landslide Susceptibility Index Determination Using Aritificial Neural Network
AU: * Kawabata, D
EM: d-kawabata@aist.go.jp
AF: Geological Survey of Japan, AIST, 1-1-1 Higashi Tsukuba Central 7, Ibaraki Prefecture, 305 8567 Japan
AU: Bandibas, J
EM: joel.bandibas@aist.go.jp
AF: Geological Survey of Japan, AIST, 1-1-1 Higashi Tsukuba Central 7, Ibaraki Prefecture, 305 8567 Japan
AU: Urai, M
EM: urai-minoru@aist.go.jp
AF: Geological Survey of Japan, AIST, 1-1-1 Higashi Tsukuba Central 7, Ibaraki Prefecture, 305 8567 Japan
AB: The occurrence of landslide is the result of the interaction of complex and diverse environmental factors. The geomorphic features, rock types and geologic structure are especially important base factors of the landslide occurrence. Generating landslide susceptibility index by defining the relationship between landslide occurrence and that base factors using conventional mathematical and statistical methods is very difficult and inaccurate. This study focuses on generating landslide susceptibility index using artificial neural networks in Southern Japanese Alps. The training data are geomorphic (e.g. altitude, slope and aspect) and geologic parameters (e.g. rock type, distance from geologic boundary and geologic dip-strike angle) and landslides. Artificial neural network structure and training scheme are formulated to generate the index. Data from areas with and without landslide occurrences are used to train the network. The network is trained to output 1 when the input data are from areas with landslides and 0 when no landslide occurred. The trained network generates an output ranging from 0 to 1 reflecting the possibility of landslide occurrence based on the inputted data. Output values nearer to 1 means higher possibility of landslide occurrence. The artificial neural network model is incorporated into the GIS software to generate a landslide susceptibility map.
DE: 9320 Asia
DE: 1719 Hydrology
DE: 1824 Geomorphology (1625)
SC: Hydrology [H]
MN: 2004 AGU Fall Meeting