Abstract:

In this paper, we apply logistic regression (LR) model (using ArcGIS10.0) and catchment units to predict the distributions of debris-flow susceptibility, taking the Benzilan-Changbo segment of the upper reach of the Jinshajiang River as an example. Overall and random case testing was conducted to validate the results. The AUC values of the LR evaluation model were based on the optimum index combination of 82.7%.The prediction results show the extremely high debris-flow susceptibility areas cover about 35.98% of the total study area, whereas the actual debris-flow area accounts for 65.03% of the total observed debris-flow area. In the case test, the ratios of actual debris-flow samples of testing dataset (in each grade partition with decreasing susceptibility levels) are 91.7%(extremely high), 75.0%(high), 36.4%(moderate), 16.7%(low) and 0(extremely low), representing favorable prediction results of the debris-flow susceptibility map. High susceptibility areas are mainly distributed in the northeastern, middle and southwestern parts of the Jinshajiang river bank. The major indexes include the distance to major highways, lithology, distance to fault zone, and average monthly precipitation in the rainy season, which indicate that human activities and seasonal rainstorm are the main triggers of debris flow in the semiarid mountainous Jinshajiang valley. We suggest that debris-flow susceptibility assessment based on LR modeling can improve the prediction accuracy of potential debris flows, and provides an important reference for forecasting, warning and preventing and mitigating debris-flow risk in semiarid mountainous valleys.

Key words: semiarid mountainous valley, debris flow, logistic regression model, susceptibility assessment, Jinshajiang River