| Summary: | 碩士 === 國立臺灣大學 === 生物環境系統工程學研究所 === 103 === Research on critical conditions for landslides has been focused on suing cumulative rainfall as the triggering hydrological factor, whereas studies on the effects of rainfall characteristics has been relatively rare. The objective of this study is to determine the effects of rainfall characteristics and physiographic factors on landslide occurrence, and to establish a model for landslide prediction. The watershed of Kaoping stream was chosen as the study area. First of all, statistical approaches were applied to test the factors for their mutual independence and their correlation with landslides. Three physiographic factors, namely slope steepness, dip slope ratio, and landslide ratio, were selected and transformed into degree of membership. Second, by analyzing how rainfall affect landslides, index of rainfall concentration degree (RDI) and index of rainfall concentration time (RTI) were defined, which can respectively reflect how and when rainfall concentrate. The aforementioned statistical approaches were also applied to cull from all the hydrologic factors RDI70, RTI70, and cumulative rainfall, which were then either normalized or transformed into degree of membership. Finally, logistic regression were applied to establish landslide warning models featuring physiographic factors and different hydrologic factors. The performances of such models were later compared.
The result shows that the model featuring RDI70 and RTI70 has the highest accuracy, indicating that RDIs and RTIs indeed help improve landslide warning models. In addition, a landslide probability contour map are provided to show the effects of rainfall characteristic factors on landslide probability. Threshold rainfall of landslide decreases when RDIs are small in value, which means rainfall is concentrated, and when RTIs are large in value, which means rainfall is concentrated in the later part of a rainfall event. These results together with the rainfall characteristic factors can be applied to hazard prediction or site rehabilitation in the future.
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