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Comparative Analysis of Machine Learning Methods and a Physical Model for Shallow Landslide Risk Modeling

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  • Lanqian Feng

    (State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Institute of Water and Soil Conservation, Chinese Academy of Sciences and Ministry of Water Resources, Yangling 712100, China
    The Research Center of Soil and Water Conservation and Ecological Environment, Chinese Academy of Sciences and Ministry of Education, Yangling 712100, China
    University of Chinese Academy of Sciences, Beijing 100049, China)

  • Mingming Guo

    (State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Institute of Water and Soil Conservation, Chinese Academy of Sciences and Ministry of Water Resources, Yangling 712100, China
    Key Laboratory of Mollisols Agroecology, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Harbin 150081, China)

  • Wenlong Wang

    (State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Institute of Water and Soil Conservation, Chinese Academy of Sciences and Ministry of Water Resources, Yangling 712100, China
    The Research Center of Soil and Water Conservation and Ecological Environment, Chinese Academy of Sciences and Ministry of Education, Yangling 712100, China
    University of Chinese Academy of Sciences, Beijing 100049, China
    Institute of Soil and Water Conservation, Northwest A&F University, Yangling 712100, China)

  • Yulan Chen

    (State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Institute of Water and Soil Conservation, Chinese Academy of Sciences and Ministry of Water Resources, Yangling 712100, China
    The Research Center of Soil and Water Conservation and Ecological Environment, Chinese Academy of Sciences and Ministry of Education, Yangling 712100, China
    University of Chinese Academy of Sciences, Beijing 100049, China)

  • Qianhua Shi

    (School of Environment and Resources, Taiyuan University of Science and Technology, Taiyuan 030024, China)

  • Wenzhao Guo

    (State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Institute of Water and Soil Conservation, Chinese Academy of Sciences and Ministry of Water Resources, Yangling 712100, China
    Institute of Soil and Water Conservation, Northwest A&F University, Yangling 712100, China)

  • Yibao Lou

    (Institute of Soil and Water Conservation, Northwest A&F University, Yangling 712100, China)

  • Hongliang Kang

    (Institute of Soil and Water Conservation, Northwest A&F University, Yangling 712100, China)

  • Zhouxin Chen

    (State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Institute of Water and Soil Conservation, Chinese Academy of Sciences and Ministry of Water Resources, Yangling 712100, China
    The Research Center of Soil and Water Conservation and Ecological Environment, Chinese Academy of Sciences and Ministry of Education, Yangling 712100, China
    University of Chinese Academy of Sciences, Beijing 100049, China)

  • Yanan Zhu

    (Institute of Soil and Water Conservation, Northwest A&F University, Yangling 712100, China)

Abstract

Shallow landslides restrict local sustainable socioeconomic development and threaten human lives and property in loess tableland. Therefore, the appropriate creation of risk maps is critical for mitigating shallow landslide disasters. The first task to be done was to evaluate the vulnerability of shallow landslides based on a machine learning model (random forest (RF), a support vector machine (SVM) and logistic regression (Log)), and a physical model (SINMAP) in the loess tableland area. By comparing the differences, the best method for evaluating the vulnerability of shallow landslide was selected. The nonlinear response relationship between shallow landslides and environmental factors was quantified based on the frequency ratio. Multicollinearity analysis was used to identify 10 factors that were applied on ML to construct the spatial distribution model. The SINMAP model used a DEM and soil physical parameters to determine the stability coefficient of the study area. The results showed that (1) shallow landslides in Dongzhiyuan mainly occurred on shady slopes with an elevation of 1068–1249 m, a slope gradient of 36°–60° and a concave shape. The stream power and stream transport indexes increased with increasing rainfall erosion, making shallow landslides likely. The susceptibility of shallow landslides changed parabolically with the change in the NDVI and mainly occurred in grassland and shrubland. (2) The four methods performed similarly in predicting the sensitivity of shallow landslides. The high-incidence areas were on both sides of eroded gully slopes. The tableland and gully bottom areas were not prone to shallow landslides. (3) The highest area under the curve (AUC) values were generated from the RF training and validation datasets of 0.92 and 0.93, respectively, followed by SVM AUC values of 0.91 and 0.92, respectively; Log AUC values of 0.91 and 0.89, respectively, and the SINMAP model AUC values of 0.69 and 0.74, respectively. In conclusion, the RF model best predicted the susceptibility of shallow landslides in the study area. The results provide a scientific basis for disaster mitigation on the Loess Plateau.

Suggested Citation

  • Lanqian Feng & Mingming Guo & Wenlong Wang & Yulan Chen & Qianhua Shi & Wenzhao Guo & Yibao Lou & Hongliang Kang & Zhouxin Chen & Yanan Zhu, 2022. "Comparative Analysis of Machine Learning Methods and a Physical Model for Shallow Landslide Risk Modeling," Sustainability, MDPI, vol. 15(1), pages 1-18, December.
    • Handle: RePEc:gam:jsusta:v:15:y:2022:i:1:p:6-:d:1008542
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    References listed on IDEAS

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    1. Wei Lin & Kunlong Yin & Ningtao Wang & Yong Xu & Zizheng Guo & Yuanyao Li, 2021. "Landslide hazard assessment of rainfall-induced landslide based on the CF-SINMAP model: a case study from Wuling Mountain in Hunan Province, China," 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. 106(1), pages 679-700, March.
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    3. Jean Baptiste Nsengiyumva & Geping Luo & Egide Hakorimana & Richard Mind'je & Aboubakar Gasirabo & Valentine Mukanyandwi, 2019. "Comparative Analysis of Deterministic and Semiquantitative Approaches for Shallow Landslide Risk Modeling in Rwanda," Risk Analysis, John Wiley & Sons, vol. 39(11), pages 2576-2595, November.
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