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Application of Tree-Based Ensemble Models to Landslide Susceptibility Mapping: A Comparative Study

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  • Aihua Wei

    (Hebei Province Key Laboratory of Sustained Utilization and Development of Water Resources, Hebei GEO University, Shijiazhuang 050031, China
    School of Water Resources and Environment, Hebei GEO University, Shijiazhuang 050031, China
    Hebei Center for Ecological and Environmental Geology Research, Hebei GEO University, Shijiazhuang 050031, China)

  • Kaining Yu

    (Hebei Province Key Laboratory of Sustained Utilization and Development of Water Resources, Hebei GEO University, Shijiazhuang 050031, China
    School of Water Resources and Environment, Hebei GEO University, Shijiazhuang 050031, China
    Hebei Center for Ecological and Environmental Geology Research, Hebei GEO University, Shijiazhuang 050031, China)

  • Fenggang Dai

    (Hebei Province Key Laboratory of Sustained Utilization and Development of Water Resources, Hebei GEO University, Shijiazhuang 050031, China
    School of Water Resources and Environment, Hebei GEO University, Shijiazhuang 050031, China
    Hebei Center for Ecological and Environmental Geology Research, Hebei GEO University, Shijiazhuang 050031, China)

  • Fuji Gu

    (Hebei Key Laboratory of Geological Resources and Environment Monitoring and Protection, Shijiazhuang 050021, China)

  • Wanxi Zhang

    (Hebei Key Laboratory of Geological Resources and Environment Monitoring and Protection, Shijiazhuang 050021, China)

  • Yu Liu

    (Hebei Key Laboratory of Geological Resources and Environment Monitoring and Protection, Shijiazhuang 050021, China)

Abstract

Ensemble machine learning methods have been widely used for modeling landslide susceptibility, but there has been no uniform ensemble method for this problem. The main objective of this study is to compare popular ensemble machine learning-based models and apply them to landslides susceptibility mapping. The selected models include the random forest (RF), which is a typical bagging ensemble model, and three advanced boosting models, namely, adaptive boosting (AB), gradient boosting decision trees (GBDT), and extreme gradient boosting (XGBoost). This study considers 94 landslide points and 12 affecting factors. The data are divided into a training dataset consisting of 70% of the overall data, and a validation dataset, containing the remaining 30% of the data. The models are evaluated using the area under the receiver operating characteristic curve (AUC) and three common performance metrics: sensitivity, specificity, and accuracy. The results indicate that the four ensemble models have an AUC of more than 0.8, suggesting that they can appropriately and accurately predict landslide susceptibility maps. In particular, the XGBoost model achieves the best performance among all models, having a sensitivity of 92.86, specificity of 90.00, and accuracy of 91.38. Furthermore, the bagging model has a sensitivity of 89.29, specificity of 86.67, and accuracy of 87.93, and it is superior to the GBDT, which achieves a sensitivity of 86.21, specificity of 86.21, and accuracy of 86.21, and the AB, reaching a sensitivity of 82.14, specificity of 80.00, and accuracy of 81.03. The results presented in this study indicate that the advanced ensemble model, the XGBoost model, could be a promising tool for the selection of ensemble models for predicting landslide susceptibility mapping.

Suggested Citation

  • Aihua Wei & Kaining Yu & Fenggang Dai & Fuji Gu & Wanxi Zhang & Yu Liu, 2022. "Application of Tree-Based Ensemble Models to Landslide Susceptibility Mapping: A Comparative Study," Sustainability, MDPI, vol. 14(10), pages 1-15, May.
  • Handle: RePEc:gam:jsusta:v:14:y:2022:i:10:p:6330-:d:821644
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    References listed on IDEAS

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    1. Dimitris Kouhartsiouk & Skevi Perdikou, 2021. "The application of DInSAR and Bayesian statistics for the assessment of landslide susceptibility," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 105(3), pages 2957-2985, February.
    2. Jules Maurice Habumugisha & Ningsheng Chen & Mahfuzur Rahman & Md Monirul Islam & Hilal Ahmad & Ahmed Elbeltagi & Gitika Sharma & Sharmina Naznin Liza & Ashraf Dewan, 2022. "Landslide Susceptibility Mapping with Deep Learning Algorithms," Sustainability, MDPI, vol. 14(3), pages 1-22, February.
    3. Hyuck-Jin Park & Kang-Min Kim & In-Tak Hwang & Jung-Hyun Lee, 2022. "Regional Landslide Hazard Assessment Using Extreme Value Analysis and a Probabilistic Physically Based Approach," Sustainability, MDPI, vol. 14(5), pages 1-17, February.
    4. Halil Akinci & Mustafa Zeybek, 2021. "Comparing classical statistic and machine learning models in landslide susceptibility mapping in Ardanuc (Artvin), Turkey," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 108(2), pages 1515-1543, September.
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    1. Esteban Bravo-López & Tomás Fernández Del Castillo & Chester Sellers & Jorge Delgado-García, 2023. "Analysis of Conditioning Factors in Cuenca, Ecuador, for Landslide Susceptibility Maps Generation Employing Machine Learning Methods," Land, MDPI, vol. 12(6), pages 1-28, May.
    2. Huadan Fan & Yuefeng Lu & Yulong Hu & Jun Fang & Chengzhe Lv & Changqing Xu & Xinyi Feng & Yanru Liu, 2022. "A Landslide Susceptibility Evaluation of Highway Disasters Based on the Frequency Ratio Coupling Model," Sustainability, MDPI, vol. 14(13), pages 1-17, June.
    3. Yanrong Liu & Zhongqiu Meng & Lei Zhu & Di Hu & Handong He, 2023. "Optimizing the Sample Selection of Machine Learning Models for Landslide Susceptibility Prediction Using Information Value Models in the Dabie Mountain Area of Anhui, China," Sustainability, MDPI, vol. 15(3), pages 1-23, January.

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