Combining a class-weighted algorithm and machine learning models in landslide susceptibility mapping: A case study of Wanzhou section of the Three Gorges Reservoir, China. (January 2022)

• Record Type: Journal Article
• Authors: Zhang, Huijuan; Song, Yingxu; Xu, Shiluo; He, Yueshun; Li, Zhiwen; Yu, Xianyu; Liang, Ye; Wu, Weicheng; Wang, Yue
• Is Part Of: Computers & geosciences. Volume 158(2022)
• Publication Date: 2022-01
• Subjects: Landslide susceptibility mapping -- Imbalanced landslide samples -- Class-weighted algorithm -- Machine learning model -- Three gorges reservoir area; Environmental policy -- Periodicals; 550.5
• DOI: 10.1016/j.cageo.2021.104966
• Languages: English
• ISSNs: 0098-3004

Abstract

Abstract: 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. Graphical abstract: Image 1 Highlights: The imbalanced landslide samples (landslides, non-landslides) in the landslide susceptibility evaluation was emphasized. A class-weighted algorithm combined with ensemble/machine learning models were applied to the landslide susceptibility evaluation. The weighted models are applicable for solving the issue of imbalanced landslide samples.