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Assessment of Landslide-Prone Areas and Their Zonation Using Logistic Regression, LogitBoost, and NaïveBayes Machine-Learning Algorithms

Author

Listed:
  • Hamid Reza Pourghasemi

    (Department of Natural Resources and Environmental Engineering, College of Agriculture, Shiraz University, Shiraz 71441-65186, Iran)

  • Amiya Gayen

    (Department of Geography, University of Gour Banga, Malda 732103, India)

  • Sungjae Park

    (Division of Science Education, Kangwon National University, 1 Kangwondaehak-gil, Chuncheon-si, Gangwon-do 24341 Korea)

  • Chang-Wook Lee

    (Division of Science Education, Kangwon National University, 1 Kangwondaehak-gil, Chuncheon-si, Gangwon-do 24341 Korea)

  • Saro Lee

    (Division of Geoscience Platform, Korea Institute of Geoscience and Mineral Resources (KIGAM), 124 Gwahang-no, Yuseong-gu, Daejeon 305-350, Korea
    Department of Geophysical Exploration, Korea University of Science and Technology, 217 Gajeong-roYuseong-gu, Daejeon 305-350, Korea)

Abstract

The occurrence of landslide in the hilly region of South Korea is a matter of serious concern. This study tries to produce landslide susceptibility maps for Jumunjin Country in South Korea. Three machine learning algorithms, namely Logistic Regression (LR), LogitBoost (LB), and NaïveBayes (NB) are used, and their final model outcomes are compared to each other. Firstly, a landslide inventory map and the associated input data layers of the landslide conditioning factors were developed based on field verification, historical records, and high-resolution remote-sensing data in the geographic information system (GIS) environment. Seventeen landslide conditioning factors were prepared, including aspect, slope, altitude, maximum curvature, profile curvature, topographic wetness index (TWI), topographic positioning index (TPI), distance from fault, convexity, forest type, forest diameter, forest density, land use/land cover, lithology, soil, flow accumulation, and mid slope position. The result showed that the area under the curve (AUC) values of LR, LB, and NB models were 84.2%, 70.7%, and 85.2%, respectively. The results revealed that the LR and LB models produced reasonable accuracy than respect to NB model in landslide susceptibility assessment. The final susceptibility maps would be useful for preliminary land-use planning and hazard mitigation purpose.

Suggested Citation

  • Hamid Reza Pourghasemi & Amiya Gayen & Sungjae Park & Chang-Wook Lee & Saro Lee, 2018. "Assessment of Landslide-Prone Areas and Their Zonation Using Logistic Regression, LogitBoost, and NaïveBayes Machine-Learning Algorithms," Sustainability, MDPI, vol. 10(10), pages 1-23, October.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:10:p:3697-:d:175740
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    References listed on IDEAS

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    1. H. Pourghasemi & H. Moradi & S. Fatemi Aghda, 2013. "Landslide susceptibility mapping by binary logistic regression, analytical hierarchy process, and statistical index models and assessment of their performances," 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. 69(1), pages 749-779, October.
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