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Rainfall Induced Landslide Susceptibility Mapping Based on Bayesian Optimized Random Forest and Gradient Boosting Decision Tree Models—A Case Study of Shuicheng County, China

by 1,2,3, 1,2,3, 1,2,3, 1,2,3, 1,2,3,*, 4 and 5,6
1
School of Environment, Northeast Normal University, Changchun 130024, China
2
Key Laboratory for Vegetation Ecology, Ministry of Education, Changchun 130117, China
3
State Environmental Protection Key Laboratory of Wetland Ecology and Vegetation Restoration, Northeast Normal University, Changchun 130024, China
4
School of Emergency Management, Changchun Institute of Technology, Changchun 130012, China
5
College of Environment and Civil Engineering, Chengdu University of Technology, Chengdu 610059, China
6
State Key Laboratory of Geohazard Prevention and Geo Environment Protection, Chengdu University of Technology, Chengdu 610059, China
*
Author to whom correspondence should be addressed.
Water 2020, 12(11), 3066; https://doi.org/10.3390/w12113066
Received: 29 September 2020 / Revised: 26 October 2020 / Accepted: 29 October 2020 / Published: 2 November 2020
(This article belongs to the Special Issue Water-Induced Landslides: Prediction and Control)
Among the most frequent and dangerous natural hazards, landslides often result in huge casualties and economic losses. Landslide susceptibility mapping (LSM) is an excellent approach for protecting and reducing the risks by landslides. This study aims to explore the performance of Bayesian optimization (BO) in the random forest (RF) and gradient boosting decision tree (GBDT) model for LSM and applied in Shuicheng County, China. Multiple data sources are used to obtain 17 conditioning factors of landslides, Borderline-SMOTE and Randomundersample methods are combined to solve the imbalanced sample problem. RF and GBDT models before and after BO are adopted to calculate the susceptibility value of landslides and produce LSMs and these models were compared and evaluated using multiple validation approach. The results demonstrated that the models we proposed all have high enough model accuracy to be applied to produce LSM, the performance of the RF is better than the GBDT model without BO, while after adopting the Bayesian optimized hyperparameters, the prediction accuracy of the RF and GBDT models is improved by 1% and 7%, respectively and the Bayesian optimized GBDT model is the best for LSM in this four models. In summary, the Bayesian optimized RF and GBDT models, especially the GBDT model we proposed for landslide susceptibility assessment and LSM construction has a very good application performance and development prospects. View Full-Text
Keywords: landslide susceptibility mapping; imbalanced sample; Bayesian optimization; random forest; gradient boosting decision tree landslide susceptibility mapping; imbalanced sample; Bayesian optimization; random forest; gradient boosting decision tree
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MDPI and ACS Style

Rong, G.; Alu, S.; Li, K.; Su, Y.; Zhang, J.; Zhang, Y.; Li, T. Rainfall Induced Landslide Susceptibility Mapping Based on Bayesian Optimized Random Forest and Gradient Boosting Decision Tree Models—A Case Study of Shuicheng County, China. Water 2020, 12, 3066. https://doi.org/10.3390/w12113066

AMA Style

Rong G, Alu S, Li K, Su Y, Zhang J, Zhang Y, Li T. Rainfall Induced Landslide Susceptibility Mapping Based on Bayesian Optimized Random Forest and Gradient Boosting Decision Tree Models—A Case Study of Shuicheng County, China. Water. 2020; 12(11):3066. https://doi.org/10.3390/w12113066

Chicago/Turabian Style

Rong, Guangzhi; Alu, Si; Li, Kaiwei; Su, Yulin; Zhang, Jiquan; Zhang, Yichen; Li, Tiantao. 2020. "Rainfall Induced Landslide Susceptibility Mapping Based on Bayesian Optimized Random Forest and Gradient Boosting Decision Tree Models—A Case Study of Shuicheng County, China" Water 12, no. 11: 3066. https://doi.org/10.3390/w12113066

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