期刊
COMPUTERS & GEOSCIENCES
卷 158, 期 -, 页码 -出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.cageo.2021.104966
关键词
Landslide susceptibility mapping; Imbalanced landslide samples; Class-weighted algorithm; Machine learning model; Three gorges reservoir area
资金
- Key Laboratory for Digital Land and Resources of Jiangxi Province, East China University of Technology [DLLJ202112]
- East China University of Technology Doctoral Research Startup Fund [DHBK2019218]
- Jiangxi Provincial Nuclear and Geoscience DataScience and System Engineering Technology Research Center [JETRCNGDSS202002]
- National Natural Science Foundation of China [41807297]
- Jiangxi Engineering Laboratory on Radioactive Geoscience and Big Data Technology [JELRGBDT202004]
- Project Digital frequency spectrum analysis and mineralization precise prediction for continental supergene U-Re [41872243]
This study investigates the application of a class-weighted algorithm with LR, LightGBM, and RF models in landslide susceptibility evaluation. Results show that the weighted models outperform the unweighted ones, with the WRF model exhibiting the best performance. The insights from this research will be useful for improving landslide susceptibility mapping and prevention strategies in the Wanzhou section.
This study aims to investigate the application of a class-weighted algorithm combined with conventional machine learning model (logistic regression (LR)) and ensemble machine learning models (LightGBM and random forest (RF)) to the landslide susceptibility evaluation. Wanzhou section of the Three Gorges Reservoir area, China, frequently suffering numerous landslides, is chosen as an example. The class-weighted algorithm focuses on the class-imbalanced issue of landslide and non-landslide samples, and it can turn the class-imbalanced issue into a cost-sensitive machine learning by setting unequal weights for different classes, which contribute to improving the accuracy of landslide susceptibility evaluation. The landslide inventory database was produced by field investigation and remote sensing images derived from Google Earth. Of the 233 landslides in the inventory, 40% were used for validation, and the remaining 60% were used for training purposes. Twelve environmental parameters (elevation, slope, aspect, curvature, distance to river, NDVI, NDWI, rainfall, seismic intensity, land use, TRI, lithology) were treated as inputs of the models to produce a landslide susceptibility map (LSM). The AUC value, Balanced accuracy, and Geometric mean score were utilized to estimate the quality of models. The result shows that the weighted models (weighted logistic regression (WLR), weighted LightGBM (WLightGBM), weighted random forest (WRF) have higher AUC values, Balanced accuracy, and Geometric mean scores than those of unweighted methods, which demonstrates that the weighted models exhibit better than unweighted models, with the WRF model having the best performance. The landslide susceptibility map of the Wanzhou section displays that the high and very high landslide susceptibility zones are mainly distributed on both sides of the river. The insights from this research will be useful for ameliorating the landslide susceptibility mapping and the prevention and mitigation for the Wanzhou section.
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