| dc.description.abstract | Artificial neural network method had not been applied to regional landslide susceptibility analysis until Lee（2003）which first used this method to evaluate landslide hazard and combine it with the multivariate analysis to construct a landslide susceptibility map. Lin（2003） utilized the artificial neural network and a fuzzy theory to produce a continuous spectrum to indicate landslide susceptibility and use this to draw a landslide susceptibility map. This study follows Lin’s method and tries to refine the fuzzy neural network system in order to learn the mechanism of landslide and to predict the location that a landslide may happen. This study adopts the Rprop algorithm to significantly reduce the long training time in the artificial neural network. Comparing the result with that of two multivariate methods validates that the fuzzy neural network system is suitable for a landslide susceptibility analysis.
This study refers to the work scheme of the landslide susceptibility analysis project in the Central Geological Survey, Taiwan（CGS）, to establish the study scheme and work procedure. Following the usage of the factors in the CGS project including lithology, slope, slope aspect, terrain roughness, slope roughness, total curvature, total height, NDVI, Arias intensity and maximum hourly rain fall factors from the four triggering events the Herb typhoon, the ChiChi earthquake, the Toraji typhoon, and the Mindule typhoon, these factors were rechecked. Landslide inventory interpreted from SOPT image, was also checked by examining a series of rectified aerial photographs in GIS and in the field. This study uses random sampling to get the training samples and proceeds to establish the fuzzy neural network framework in Matlab, and then applied the trained network to the whole area. The output fuzzy membership for landslide and nonlandslide was defuzzied, to become a single value indicating landslide susceptibility. These values were used to construct a landslide susceptibility map.
The result shows that high susceptibility areas generally locate at high slope areas and fit well with the actual landslide areas that experts interpreted. The landslide ratio has a trend that higher landslide susceptibility index expresses higher landslide ratio for all terrains and for the four events.
In order to depress the noise in a linear system, more data processing is usually needed. However, the interaction among the internal nodes in the neural network can clear the noise data and get better result. A fuzzy neural network system is applicable to the landslide susceptibility analysis, and it could provide a reference for comparison with the traditional statistical methods. | en_US |