Abstract
This work was to generate landslide susceptibility maps for the Three Gorges Reservoir (TGR) area, China by using different machine learning models. Three advanced machine learning methods, namely, gradient boosting decision tree (GBDT), random forest (RF) and information value (InV) models, were used, and the performances were assessed and compared. In total, 202 landslides were mapped by using a series of field surveys, aerial photographs, and reviews of historical and bibliographical data. Nine causative factors were then considered in landslide susceptibility map generation by using the GBDT, RF and InV models. All of the maps of the causative factors were resampled to a resolution of 28.5 m. Of the 486289 pixels in the area, 28526 pixels were landslide pixels, and 457763 pixels were non-landslide pixels. Finally, landslide susceptibility maps were generated by using the three machine learning models, and their performances were assessed through receiver operating characteristic (ROC) curves, the sensitivity, specificity, overall accuracy (OA), and kappa coefficient (KAPPA). The results showed that the GBDT, RF and InV models in overall produced reasonable accurate landslide susceptibility maps. Among these three methods, the GBDT method outperforms the other two machine learning methods, which can provide strong technical support for producing landslide susceptibility maps in TGR.
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Acknowledgments
This work was supported in part by the National Natural Science Foundation of China (61601418, 41602362, 61871259), in part by the Opening Foundation of Hunan Engineering and Research Center of Natural Resource Investigation and Monitoring (2020-5), in part by the Qilian Mountain National Park Research Center (Qinghai) (grant number: GKQ2019-01), and in part by the Geomatics Technology and Application Key Laboratory of Qinghai Province, Grant No. QHDX-2019-01.
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Chen, T., Zhu, L., Niu, Rq. et al. Mapping landslide susceptibility at the Three Gorges Reservoir, China, using gradient boosting decision tree, random forest and information value models. J. Mt. Sci. 17, 670–685 (2020). https://doi.org/10.1007/s11629-019-5839-3
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DOI: https://doi.org/10.1007/s11629-019-5839-3