期刊
BULLETIN OF ENGINEERING GEOLOGY AND THE ENVIRONMENT
卷 78, 期 1, 页码 247-266出版社
SPRINGER HEIDELBERG
DOI: 10.1007/s10064-018-1256-z
关键词
Landslide; Kernel logistic regression; Naive Bayes; RBF network; China
资金
- China Postdoctoral Science Foundation [2017 M613168]
- Shaanxi Province Postdoctoral Science Foundation [2017BSHYDZZ07]
- Scientific Research Program - Shaanxi Provincial Education Department [17JK0511]
- Open Fund of Shandong Provincial Key Laboratory of Depositional Mineralization AMP
- Sedimentary Minerals [DMSM2017029]
- Open-ended Fund of the Key Laboratory for Geo-hazard in Loess Areas, Ministry of Land and Resources of China [KLGLAMLR201603]
The main goal of this study is to assess and compare three advanced machine learning techniques, namely, kernel logistic regression (KLR), naive Bayes (NB), and radial basis function network (RBFNetwork) models for landslide susceptibility modeling in Long County, China. First, a total of 171 landslide locations were identified within the study area using historical reports, aerial photographs, and extensive field surveys. All the landslides were randomly separated into two parts with a ratio of 70/30 for training and validation purposes. Second, 12 landslide conditioning factors were prepared for landslide susceptibility modeling, including slope aspect, slope angle, plan curvature, profile curvature, elevation, distance to faults, distance to rivers, distance to roads, lithology, NDVI (normalized difference vegetation index), land use, and rainfall. Third, the correlations between the conditioning factors and the occurrence of landslides were analyzed using normalized frequency ratios. A multicollinearity analysis of the landslide conditioning factors was carried out using tolerances and variance inflation factor (VIF) methods. Feature selection was performed using the chi-squared statistic with a 10-fold cross-validation technique to assess the predictive capabilities of the landslide conditioning factors. Then, the landslide conditioning factors with null predictive ability were excluded in order to optimize the landslide models. Finally, the trained KLR, NB, and RBFNetwork models were used to construct landslide susceptibility maps. The receiver operating characteristics (ROC) curve, the area under the curve (AUC), and several statistical measures, such as accuracy (ACC), F-measure, mean absolute error (MAE), and root mean squared error (RMSE), were used for the assessment, validation, and comparison of the resulting models in order to choose the best model in this study. The validation results show that all three models exhibit reasonably good performance, and the KLR model exhibits the most stable and best performance. The KLR model, which has a success rate of 0.847 and a prediction rate of 0.749, is a promising technique for landslide susceptibility mapping. Given the outcomes of the study, all three models could be used efficiently for landslide susceptibility analysis.
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