Analysis of the Occurrent Models of Potential Debris-Flow Sources in the Watershed of Ching-Shuei River

Abstract

The areas around the Ching-Shuei River saw numerous landslides (2004–2017) after the Jiji earthquake, profoundly harming the watershed’s geological environment. The 33 catchment areas in the Ching-Shuei River watershed and five typhoon and rainstorm events, with a total of 165 occurrences and non-occurrences, were analyzed, and the training and validation were categorized into 70% training and 30% validation. A landslide disaster is deemed, for the purposes of this research, to have taken place if SPOT satellite images taken before and after an incident show a Normalized Difference Vegetation Index difference larger than 0.25, a slope of less than 30 degrees, and a number of connected grids greater than 10. The analysis was carried out using the instability index method analysis with Rogers regression analysis and artificial neural network. The accuracy rates of neural network, logit regression, and instability index analyses were, respectively, 93.3%, 80.6%, and 70.9%. The neural network’s area under the curve was 0.933, indicating excellent discrimination ability; that of the logit regression analysis was 0.794, which is considered good; and that of the instability index analysis was 0.635, or fair. This suggests that any of the three models are suitable for the danger assessment of large post-earthquake debris flows. The results of this study also provide a reference and evidence for specific sites’ potential susceptibility to debris flows.

Keywords

• debris-flow disasters
• artificial neural network
• instability index method analysis
• logistic regression analysis