Vulnerability assessment of rainfall-induced debris flows in Taiwan

A GIS-based decision support system, which incorporates local topographic and rainfall effects on debris flow vulnerability is developed. Rainfall at a scale compatible with the digital elevation model resolution is obtained using a neural network with a wind-induced topographic effect and rainfall derived from satellite rain estimates and an adaptive inverse distance weight method (WTNN). The technique is tested using data collected during the passage of typhoon Tori-Ji on July 2001 over central Taiwan. Numerous debris flows triggered by the typhoon were used as control for the study. Our results show that the WTNN technique outperforms other interpolation techniques including adaptive inversed distance weight (AIDW), simple kriging (SK), co-kriging, and multiple linear regression using gauge, and topographic parameters. Multiple remotely-sensed, fuzzy-based debris-flow susceptibility parameters are used to describe the characteristics of watersheds. Non-linear, multi-variant regressions using the WTNN derived rainfall and topography factors are derived using self-organizing maps (SOM) for the debris flow vulnerability assessment. An index of vulnerability representing the degrees of hazard is implemented in a GIS-based decision support system by which a decision maker can assess debris flow vulnerability.