关键词: 不完备样本, 支持向量回归模型, 滑坡易发性指数, 精度分析

Abstract:

Landslide hazard susceptibility relates to middle- and long- term predicting and forecasting, and it is very important to landslide managements. In the process of evaluation based on statistical model, the result is greatly influenced by landslide sample size, so the more conservative and less influencing model must be applied to the susceptibility evaluation in order to reduce the system error. The study area is located in Malaysia tropical rainforest, where nine factors were selected as topographic slope, aspect, surface curvature, geomorphology, lithology, structure, land cover, road and drainage and so on. The Landslide Hazard Susceptibility Index (LSI) was constructed based on support vector regression (SVR) theory, then the susceptibility evaluation methodology was discussed in incomplete sample conditions, and the relation between the sample size and the result accuracy was analysed too. The result show that the success-rate analysis accuracy based on SVR model was about 95.9%, an obviously high value; the fluctuation of sample size influenced the accuracy slightly; SVR was a better model suited to landslide hazard evaluation in high vegetation cover conditions, which could provide a technique support for landslide management in similar areas.

Key words: incomplete sample, SVR model, landslide susceptibility index, accuracy analysis