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Landslide Susceptibility Assessment Using Spatial Multi-Criteria Evaluation Model in Rwanda

Author

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  • Jean Baptiste Nsengiyumva

    (State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, No. 818, South Beijing Road, Urumqi 830011, China
    University of Chinese Academy of Sciences, Beijing 100049, China
    Ministry of Disaster Management and Refugees (MIDIMAR), P.O. Box 4386, Kigali 00250, Rwanda)

  • Geping Luo

    (State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, No. 818, South Beijing Road, Urumqi 830011, China
    University of Chinese Academy of Sciences, Beijing 100049, China)

  • Lamek Nahayo

    (State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, No. 818, South Beijing Road, Urumqi 830011, China
    University of Chinese Academy of Sciences, Beijing 100049, China)

  • Xiaotao Huang

    (State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, No. 818, South Beijing Road, Urumqi 830011, China
    University of Chinese Academy of Sciences, Beijing 100049, China)

  • Peng Cai

    (State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, No. 818, South Beijing Road, Urumqi 830011, China
    University of Chinese Academy of Sciences, Beijing 100049, China)

Abstract

Landslides susceptibility assessment has to be conducted to identify prone areas and guide risk management. Landslides in Rwanda are very deadly disasters. The current research aimed to conduct landslide susceptibility assessment by applying Spatial Multi-Criteria Evaluation Model with eight layers of causal factors including: slope, distance to roads, lithology, precipitation, soil texture, soil depth, altitude and land cover. In total, 980 past landslide locations were mapped. The relationship between landslide factors and inventory map was calculated using the Spatial Multi-Criteria Evaluation. The results revealed that susceptibility is spatially distributed countrywide with 42.3% of the region classified from moderate to very high susceptibility, and this is inhabited by 49.3% of the total population. In addition, Provinces with high to very high susceptibility are West, North and South (40.4%, 22.8% and 21.5%, respectively). Subsequently, the Eastern Province becomes the peak under low susceptibility category (87.8%) with no very high susceptibility (0%). Based on these findings, the employed model produced accurate and reliable outcome in terms of susceptibility, since 49.5% of past landslides fell within the very high susceptibility category, which confirms the model’s performance. The outcomes of this study will be useful for future initiatives related to landslide risk reduction and management.

Suggested Citation

  • Jean Baptiste Nsengiyumva & Geping Luo & Lamek Nahayo & Xiaotao Huang & Peng Cai, 2018. "Landslide Susceptibility Assessment Using Spatial Multi-Criteria Evaluation Model in Rwanda," IJERPH, MDPI, vol. 15(2), pages 1-23, January.
  • Handle: RePEc:gam:jijerp:v:15:y:2018:i:2:p:243-:d:129664
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    References listed on IDEAS

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    Cited by:

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    2. Martin Kuradusenge & Santhi Kumaran & Marco Zennaro, 2020. "Rainfall-Induced Landslide Prediction Using Machine Learning Models: The Case of Ngororero District, Rwanda," IJERPH, MDPI, vol. 17(11), pages 1-20, June.
    3. Sk Ajim Ali & Farhana Parvin & Quoc Bao Pham & Khaled Mohamed Khedher & Mahro Dehbozorgi & Yasin Wahid Rabby & Duong Tran Anh & Duc Hiep Nguyen, 2022. "An ensemble random forest tree with SVM, ANN, NBT, and LMT for landslide susceptibility mapping in the Rangit River watershed, India," 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. 113(3), pages 1601-1633, September.

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