Construction of a sediment disaster risk assessment model

This study employed genetic adaptive neural networks in the classification of high-resolution satellite images from which data related to surface conditions in mountainous areas of Taiwan were derived. Principal component analysis was then used to extract factors associated with the threat of natural disaster, and logistic regression was used to compute the probability of disaster occurrence. Through field surveys, interviews with district officials and a review of relevant literature, the probability of a sediment disaster was estimated as well as the vulnerability of the villages concerned and the degree to which these villages were prepared, to construct a risk evaluation model. A geographic information system was used to plot regional risk maps as a means to enhance the safety of residents in the study area. The risk assessment model can be used by authorities to make provisions for high-risk areas, to reduce the number of casualties and social costs of sediment disasters.