期刊
GEOMATICS NATURAL HAZARDS & RISK
卷 8, 期 2, 页码 1997-2022出版社
TAYLOR & FRANCIS LTD
DOI: 10.1080/19475705.2017.1403974
关键词
Landslide; GIS; geographically weighted regression; remote sensing; support vector machine
资金
- National Natural Science Foundation of China [41472202, 41401032]
- CAS-VPST Silk Road Science Fund [GJHZ1855]
- General Programme of Jiangxi Meteorological Bureau
This study evaluated the geographically weighted regression (GWR) model for landslide susceptibility mapping in Xing Guo County, China. In this study, 16 conditioning factors, such as slope, aspect, altitude, topographic wetness index, stream power index, sediment transport index, soil, lithology, normalized difference vegetation index (NDVI), landuse, rainfall, distance to road, distance to river, distance to fault, plan curvature, and profile curvature, were analyzed. Chi-square feature selection method was adopted to compare the significance of each factor with landslide occurence. The GWR model was compared with two well-known models, namely, logistic regression (LR) and support vcector machine (SVM). Results of chi-square feature selection indicated that lithology and slope are the most influencial factors, whereas SPI was found statistically insignificant. Four landslide susceptibility maps were generated by GWR, SGD-LR, SGD-SVM, and SVM models. The GWR model exhibited the highest performance in terms of success rate and prediction accuracy, with values of 0.789 and 0.819, respectively. The SVM model exhibited slightly lower AUC values than that of the GWR model. Validation result of the four models indicates that GWR is a better model than other widely used models.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据