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Improved Shallow Landslide Susceptibility Prediction Based on Statistics and Ensemble Learning

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  • Zhu Liang

    (College of Construction Engineering, South China University of Technology, Guangzhou 510641, China
    Guangzhou Urban Planning & Design Survey Research Institute, Guangzhou 510060, China
    Guangdong Enterprise Key Laboratory for Urban Sensing, Monitoring and Early Warning, Guangzhou 510060, China)

  • Wei Liu

    (Guangzhou Urban Planning & Design Survey Research Institute, Guangzhou 510060, China
    Guangdong Enterprise Key Laboratory for Urban Sensing, Monitoring and Early Warning, Guangzhou 510060, China)

  • Weiping Peng

    (Guangzhou Urban Planning & Design Survey Research Institute, Guangzhou 510060, China
    Guangdong Enterprise Key Laboratory for Urban Sensing, Monitoring and Early Warning, Guangzhou 510060, China)

  • Lingwei Chen

    (Guangzhou Urban Planning & Design Survey Research Institute, Guangzhou 510060, China
    Guangdong Enterprise Key Laboratory for Urban Sensing, Monitoring and Early Warning, Guangzhou 510060, China)

  • Changming Wang

    (College of Construction Engineering, Jilin University, Changchun 130012, China)

Abstract

Rainfall-induced landslides bring great damage to human life in mountain areas. Landslide susceptibility assessment (LSA) as an essential step toward landslide prevention has attacked a considerate focus for years. However, defining a reliable or accurate susceptibility model remains a challenge although various methods have been applied. The main purpose of this paper is to explore a comprehensive model with high reliability, accuracy, and intelligibility in LSA by combing statistical methods and ensemble learning techniques. Miyun country in Beijing is selected as the study area. Firstly, the dataset containing 370 landslide locations inventories and 13 conditioning factors were collected and non-landslide samples were prepared by clustering analysis. Secondly, random forest (RF), gradient boosting decision tree (GBDT), and adaptive boosting decision tree (Ada-DT) were selected as base learners for the Stacking ensemble method, and these methods were evaluated using measures like area under the curve (AUC). Finally, the Gini index and frequent ratio (FR) were combined to analyze the major conditioning factors. The results indicated that the performance of the Stacking method was enhanced with an AUC value of 0.944 while the basic classifiers also performed well with 0.906, 0.910, and 0.917 for RF, GBDT, and Ada-DT, respectively. Regions with a distance to a stream less than 2000 m, a distance to a road less than 3000 m, and elevation less than 600 m were susceptible to the landslide hazard. The conclusion demonstrates that the performance of LSA desires enhancement and the reliability and intelligibility of a model can be improved by combining binary and multivariate statistical methods.

Suggested Citation

  • Zhu Liang & Wei Liu & Weiping Peng & Lingwei Chen & Changming Wang, 2022. "Improved Shallow Landslide Susceptibility Prediction Based on Statistics and Ensemble Learning," Sustainability, MDPI, vol. 14(10), pages 1-21, May.
  • Handle: RePEc:gam:jsusta:v:14:y:2022:i:10:p:6110-:d:817909
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    References listed on IDEAS

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    1. Chang-Jo Chung & Andrea Fabbri, 2003. "Validation of Spatial Prediction Models for Landslide Hazard Mapping," 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. 30(3), pages 451-472, November.
    2. Schratz, Patrick & Muenchow, Jannes & Iturritxa, Eugenia & Richter, Jakob & Brenning, Alexander, 2019. "Hyperparameter tuning and performance assessment of statistical and machine-learning algorithms using spatial data," Ecological Modelling, Elsevier, vol. 406(C), pages 109-120.
    3. Paolo Magliulo & Antonio Di Lisio & Filippo Russo & Antonio Zelano, 2008. "Geomorphology and landslide susceptibility assessment using GIS and bivariate statistics: a case study in southern Italy," 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. 47(3), pages 411-435, December.
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