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Landslide susceptibility and influencing factors analysis in Rwanda

Author

Listed:
  • Richard Mind’je

    (Chinese Academy of Sciences
    Chinese Academy of Sciences
    University of Chinese Academy of Sciences
    University of Lay Adventists of Kigali (UNILAK))

  • Lanhai Li

    (Chinese Academy of Sciences
    Chinese Academy of Sciences
    University of Chinese Academy of Sciences
    CAS Research Center for Ecology and Environment of Central Asia)

  • Jean Baptiste Nsengiyumva

    (Chinese Academy of Sciences
    University of Chinese Academy of Sciences)

  • Christophe Mupenzi

    (University of Lay Adventists of Kigali (UNILAK))

  • Enan Muhire Nyesheja

    (University of Lay Adventists of Kigali (UNILAK))

  • Patient Mindje Kayumba

    (University of Chinese Academy of Sciences
    University of Lay Adventists of Kigali (UNILAK))

  • Aboubakar Gasirabo

    (Chinese Academy of Sciences
    University of Chinese Academy of Sciences
    University of Lay Adventists of Kigali (UNILAK))

  • Egide Hakorimana

    (Chinese Academy of Sciences
    University of Chinese Academy of Sciences
    University of Lay Adventists of Kigali (UNILAK))

Abstract

Rwanda as a landlocked country has been recurrently facing tremendous and devastating landslides having serious impacts on the environment and socioeconomic development. Analyzing landslide susceptibility and its influencing factors could ease landslide disaster control and, hence, contributes to environmental sustainability. Unfortunately, detailed information on influencing factors per respective classes and their level of correlation to the probability of landslide occurrence have not been fully analyzed in Rwanda. The purpose of this study is to reveal the spatial correlation between landslide occurrence and different classes of influencing factors using the frequency ratio (FR) approach with geographical information system (GIS) and remote sensing techniques. Initially, a landslide inventory map was prepared using 423 landslide locations that were randomly split into 75% of training datasets (318 points) and 25% (105 points) to validate the model. A multicollinearity analysis was performed among ten influencing factors using the tolerance and variance inflation factor method. These factors include elevation, slope, aspect, distance to roads, distance to rivers, the normalized difference vegetation index, land use land cover, stream power index, rainfall and soil texture. The analysis revealed no multicollinearity among these factors, and therefore, all of them were suitable for modeling process. The FR model evaluated the relationship between landslide incidence and influencing factors in their respective classes and then generated the landslide susceptibility map using GIS which revealed the western, northern and some parts of southern province as the most susceptible areas owing to the slope, LCLU, rainfall and elevation as the main influencing factors. The generated susceptibility map was validated using the area under curve which portrayed 81.2% and 84.6% for success and prediction rate, respectively. To conclude, the results of this study are essential for future initiatives regarding landslide risk reduction toward sustainability in Rwanda. However, these can be broad and may reflect the most landslide influencing factors projected in other central-east African regions.

Suggested Citation

  • Richard Mind’je & Lanhai Li & Jean Baptiste Nsengiyumva & Christophe Mupenzi & Enan Muhire Nyesheja & Patient Mindje Kayumba & Aboubakar Gasirabo & Egide Hakorimana, 2020. "Landslide susceptibility and influencing factors analysis in Rwanda," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 22(8), pages 7985-8012, December.
    • Handle: RePEc:spr:endesu:v:22:y:2020:i:8:d:10.1007_s10668-019-00557-4
    DOI: 10.1007/s10668-019-00557-4
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    2. Esteban Bravo-López & Tomás Fernández Del Castillo & Chester Sellers & Jorge Delgado-García, 2023. "Analysis of Conditioning Factors in Cuenca, Ecuador, for Landslide Susceptibility Maps Generation Employing Machine Learning Methods," Land, MDPI, vol. 12(6), pages 1-28, May.
    3. Liying Sun & Bingjuan Ma & Liang Pei & Xiaohang Zhang & John L. Zhou, 2021. "The relationship of human activities and rainfall-induced landslide and debris flow hazards in Central 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. 107(1), pages 147-169, May.
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