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A Support Vector Machine for Landslide Susceptibility Mapping in Gangwon Province, Korea

Author

Listed:
  • Saro Lee

    (Geological Research Division, Korea Institute of Geoscience and Mineral Resources (KIGAM), Daejeon 305350, Korea
    Department of Geophysical Exploration, Korea University of Science and Technology, Daejeon 305350, Korea)

  • Soo-Min Hong

    (Department of English Language and Literature, University of Seoul, Seoul 02504, Korea)

  • Hyung-Sup Jung

    (Department of Geoinformatics, University of Seoul, Seoul 02504, Korea)

Abstract

In this study, the support vector machine (SVM) was applied and validated by using the geographic information system (GIS) in order to map landslide susceptibility. In order to test the usefulness and effectiveness of the SVM, two study areas were carefully selected: the PyeongChang and Inje areas of Gangwon Province, Korea. This is because, not only did many landslides (2098 in PyeongChang and 2580 in Inje) occur in 2006 as a result of heavy rainfall, but the 2018 Winter Olympics will be held in these areas. A variety of spatial data, including landslides, geology, topography, forest, soil, and land cover, were identified and collected in the study areas. Following this, the spatial data were compiled in a GIS-based database through the use of aerial photographs. Using this database, 18 factors relating to topography, geology, soil, forest and land use, were extracted and applied to the SVM. Next, the detected landslide data were randomly divided into two sets; one for training and the other for validation of the model. Furthermore, a SVM, specifically a type of data-mining classification model, was applied by using radial basis function kernels. Finally, the estimated landslide susceptibility maps were validated. In order to validate the maps, sensitivity analyses were carried out through area-under-the-curve analysis. The achieved accuracies from the SVM were approximately 81.36% and 77.49% in the PyeongChang and Inje areas, respectively. Moreover, a sensitivity assessment of the factors was performed. It was found that all of the factors, except for soil topography, soil drainage, soil material, soil texture, timber diameter, timber age, and timber density for the PyeongChang area, and timber diameter, timber age, and timber density for the Inje area, had relatively positive effects on the landslide susceptibility maps. These results indicate that SVMs can be useful and effective for landslide susceptibility analysis.

Suggested Citation

  • Saro Lee & Soo-Min Hong & Hyung-Sup Jung, 2017. "A Support Vector Machine for Landslide Susceptibility Mapping in Gangwon Province, Korea," Sustainability, MDPI, vol. 9(1), pages 1-15, January.
    • Handle: RePEc:gam:jsusta:v:9:y:2017:i:1:p:48-:d:86655
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    Citations

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    Cited by:

    1. Soyoung Park & Se-Yeong Hamm & Jinsoo Kim, 2019. "Performance Evaluation of the GIS-Based Data-Mining Techniques Decision Tree, Random Forest, and Rotation Forest for Landslide Susceptibility Modeling," Sustainability, MDPI, vol. 11(20), pages 1-20, October.
    2. Ke Luo & Yingying Jiao, 2021. "Automatic fault detection of sensors in leather cutting control system under GWO-SVM algorithm," PLOS ONE, Public Library of Science, vol. 16(3), pages 1-24, March.
    3. 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.
    4. Sunmin Lee & Yunjung Hyun & Moung-Jin Lee, 2019. "Groundwater Potential Mapping Using Data Mining Models of Big Data Analysis in Goyang-si, South Korea," Sustainability, MDPI, vol. 11(6), pages 1-21, March.
    5. Jihye Han & Soyoung Park & Seongheon Kim & Sanghun Son & Seonghyeok Lee & Jinsoo Kim, 2019. "Performance of Logistic Regression and Support Vector Machines for Seismic Vulnerability Assessment and Mapping: A Case Study of the 12 September 2016 ML5.8 Gyeongju Earthquake, South Korea," Sustainability, MDPI, vol. 11(24), pages 1-19, December.
    6. Majid Mohammady, 2023. "Badland erosion susceptibility mapping using machine learning data mining techniques, Firozkuh watershed, Iran," 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. 117(1), pages 703-721, May.
    7. Kyungjin An & Suyeon Kim & Taebyeong Chae & Daeryong Park, 2018. "Developing an Accessible Landslide Susceptibility Model Using Open-Source Resources," Sustainability, MDPI, vol. 10(2), pages 1-13, January.
    8. Laura Turconi & Fabio Luino & Mattia Gussoni & Francesco Faccini & Marco Giardino & Marco Casazza, 2019. "Intrinsic Environmental Vulnerability as Shallow Landslide Susceptibility in Environmental Impact Assessment," Sustainability, MDPI, vol. 11(22), pages 1-22, November.
    9. Aliasghar Azma & Esmaeil Narreie & Abouzar Shojaaddini & Nima Kianfar & Ramin Kiyanfar & Seyed Mehdi Seyed Alizadeh & Afshin Davarpanah, 2021. "Statistical Modeling for Spatial Groundwater Potential Map Based on GIS Technique," Sustainability, MDPI, vol. 13(7), pages 1-18, March.

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