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Developing an Accessible Landslide Susceptibility Model Using Open-Source Resources

Author

Listed:
  • Kyungjin An

    (Department of Forestry and Landscape Architecture, Konkuk University, Seoul 05029, Korea)

  • Suyeon Kim

    (Department of Environmental Science, Graduate School, Konkuk University, Seoul 05029, Korea)

  • Taebyeong Chae

    (Satellite Information Promotion Team, National Satellite Operation & Application Center, Korea Aerospace Research Institute (KARI), Daejeon 34133, Korea)

  • Daeryong Park

    (Department of Civil and Environmental Engineering, Konkuk University, Seoul 05029, Korea)

Abstract

Landslide susceptibility models are important for public safety, but often rely on inaccessible or unaffordable software and geospatial data. Thus, affordable and accessible landslide prediction systems would be especially useful in places that lack the infrastructure for acquiring and analyzing geospatial data. Current landslide susceptibility models and existing methodologies do not consider such issues; therefore, this study aimed to develop an accessible and affordable landslide susceptibility modeling application and methodology based on open-source software and geospatial data. This model used TRIGRS (asc format) and QGIS (Digital Elevation Models (DEMs) extracted from GeoTIFF format) with widely accessible environmental parameters to identify potential landslide risks. In order to verify the suitability of the proposed application and methodology, a case study was conducted on Lantau Island, Hong Kong to assess the validity of the results, a comparison with 1999 landslide locations. The application developed in this study showed a good agreement with the four previous landslide locations marked as highly susceptible, which proves the validity of the study. Therefore, the developing model and the cost-effective approach, in this study simulated the landslide performance well and suggested the new approach of the landslide prediction system.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:2:p:293-:d:128362
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    References listed on IDEAS

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    1. Binh Thai Pham & Ataollah Shirzadi & Himan Shahabi & Ebrahim Omidvar & Sushant K. Singh & Mehebub Sahana & Dawood Talebpour Asl & Baharin Bin Ahmad & Nguyen Kim Quoc & Saro Lee, 2019. "Landslide Susceptibility Assessment by Novel Hybrid Machine Learning Algorithms," Sustainability, MDPI, vol. 11(16), pages 1-25, August.
    2. Bipin Peethambaran & R. Anbalagan & K. V. Shihabudheen, 2019. "Landslide susceptibility mapping in and around Mussoorie Township using fuzzy set procedure, MamLand and improved fuzzy expert system-A comparative study," 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. 96(1), pages 121-147, March.
    3. Liulei Bao & Guangcheng Zhang & Xinli Hu & Shuangshuang Wu & Xiangdong Liu, 2021. "Stage Division of Landslide Deformation and Prediction of Critical Sliding Based on Inverse Logistic Function," Energies, MDPI, vol. 14(4), pages 1-24, February.
    4. Jiangping Gao & Xiangyang Shi & Linghui Li & Ziqiang Zhou & Junfeng Wang, 2022. "Assessment of Landslide Susceptibility Using Different Machine Learning Methods in Longnan City, China," Sustainability, MDPI, vol. 14(24), pages 1-26, December.
    5. Wei Xie & Wen Nie & Pooya Saffari & Luis F. Robledo & Pierre-Yves Descote & Wenbin Jian, 2021. "Landslide hazard assessment based on Bayesian optimization–support vector machine in Nanping City, 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. 109(1), pages 931-948, October.

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