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Landslide Susceptibility Mapping Based on Weighted Gradient Boosting Decision Tree in Wanzhou Section of the Three Gorges Reservoir Area (China)

by Yingxu Song 1,2,†, Ruiqing Niu 1,*, Shiluo Xu 3,†, Runqing Ye 4, Ling Peng 5, Tao Guo 6, Shiyao Li 1 and Tao Chen 1
1
Institute of Geophysics and Geomatics, China University of Geosciences (Wuhan), Wuhan 430074, China
2
Central-south China Geoscience Innovation Center, China Geological Survey, Wuhan 430205, China
3
School of Information Engineering, Huzhou University, Huzhou 313000, China
4
Wuhan Geological Survey Center, China Geological Survey, Wuhan 430205, China
5
China Institute of Geo-Environment Monitoring, Beijing 100081, China
6
Sichuan Zhitu Information Technology Co., Ltd., Chengdu 610000, China
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
ISPRS Int. J. Geo-Inf. 2019, 8(1), 4; https://doi.org/10.3390/ijgi8010004
Received: 31 October 2018 / Revised: 6 December 2018 / Accepted: 17 December 2018 / Published: 25 December 2018
The main goal of this study is to produce a landslide susceptibility map in the Wanzhou section of the Three Gorges reservoir area (China) with a weighted gradient boosting decision tree (weighted GBDT) model. According to the current research on landslide susceptibility mapping (LSM), the GBDT method is rarely used in LSM. Furthermore, previous studies have rarely considered the imbalance of landslide samples and simply regarded the LSM problem as a binary classification problem. In this paper, we considered LSM as an imbalanced learning problem and obtained a better predictive model using the weighted GBDT method. The innovations of the article mainly include the following two points: introducing the GBDT model into the evaluation of landslide susceptibility; using the weighted GBDT method to deal with the problem of landslide sample imbalance. The logistic regression (LR) model and gradient boosting decision tree (GBDT) model were also used in the study to compare with the weighted GBDT model. Five kinds of data from different data source were used in the study: geology, topography, hydrology, land cover, and triggered factors (rainfall, earthquake, land use, etc.). Twenty nine environmental parameters and 233 landslides were used as input data. The receiver operating characteristic (ROC) curve, the area under the ROC curve (AUC) value, and the recall value were used to estimate the quality of the weighted GBDT model, the GBDT model, and the LR model. The results showed that the GBDT model and the weighted GBDT model had a higher AUC value (0.977, 0.976) than the LR model (0.845); the weighted GBDT model had a little higher AUC value (0.977) than the GBDT model (0.976); and the weighted GBDT model had a higher recall value (0.823) than the GBDT model (0.426) and the LR model (0.004). The weighted GBDT method could be considered to have the best performance considering the AUC value and the recall value in landslide susceptibility mapping dealing with imbalanced landslide data. View Full-Text
Keywords: landslide susceptibility mapping; Three Gorges area; weighted GBDT; logistic regression; imbalanced landslide data landslide susceptibility mapping; Three Gorges area; weighted GBDT; logistic regression; imbalanced landslide data
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Song, Y.; Niu, R.; Xu, S.; Ye, R.; Peng, L.; Guo, T.; Li, S.; Chen, T. Landslide Susceptibility Mapping Based on Weighted Gradient Boosting Decision Tree in Wanzhou Section of the Three Gorges Reservoir Area (China). ISPRS Int. J. Geo-Inf. 2019, 8, 4.

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