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An optimization on machine learning algorithms for mapping snow avalanche susceptibility

Author

Listed:
  • Peyman Yariyan

    (Saghez Branch, Islamic Azad University)

  • Ebrahim Omidvar

    (University of Kashan)

  • Foad Minaei

    (Ferdowsi University of Mashhad)

  • Rahim Ali Abbaspour

    (University of Tehran)

  • John P. Tiefenbacher

    (Texas State University)

Abstract

Mapping avalanche-prone areas to mitigate damages is important and vital for safety and development planning. New hybrid models are introduced for snow avalanche susceptibility mapping (SASM) in the Zarrinehroud and Darvan watersheds in northwestern Iran. A hybrid of four learning models—radial basis function, multi-layer perceptron, fuzzy ARTMAP (or predictive adaptive resonance theory (ART), and self-organizing map (SOM)—with three statistical algorithms—frequency ratio, statistical index, and weights-of-evidence—and K-means clustering integrated 20 factors and 177 avalanche locations. The areas most likely to produce snow avalanches were identified. The relative importance of the predictive factors was determined by analyzing the information gain ratio (IGR). Slope (average merit (AM) = 0.48055) and LS (AM = 0.00202) were the most and least important factors. Positive predictive value, negative predictive value, sensitivity, specificity, area under the curve (AUC), standard error (SE), mean square error, and root mean square error (RMSE) were used to validate the results of the models. The K-means-SOM hybrid model (AUC = 0.811, SE = 0.0548, RMSE = 0.39005) produced the best results of the hybrid models. This study demonstrates that SASM can help local managers and planners mitigate losses of life and damages caused by avalanches.

Suggested Citation

  • Peyman Yariyan & Ebrahim Omidvar & Foad Minaei & Rahim Ali Abbaspour & John P. Tiefenbacher, 2022. "An optimization on machine learning algorithms for mapping snow avalanche susceptibility," 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. 111(1), pages 79-114, March.
  • Handle: RePEc:spr:nathaz:v:111:y:2022:i:1:d:10.1007_s11069-021-05045-5
    DOI: 10.1007/s11069-021-05045-5
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    References listed on IDEAS

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    1. Grant Statham & Pascal Haegeli & Ethan Greene & Karl Birkeland & Clair Israelson & Bruce Tremper & Chris Stethem & Bruce McMahon & Brad White & John Kelly, 2018. "A conceptual model of avalanche hazard," 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. 90(2), pages 663-691, January.
    2. F. Gauthier & D. Germain & B. Hétu, 2017. "Logistic models as a forecasting tool for snow avalanches in a cold maritime climate: northern Gaspésie, Québec, Canada," 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. 89(1), pages 201-232, October.
    3. Majid Mohammady & Hamid Reza Pourghasemi & Mojtaba Amiri, 2019. "Assessment of land subsidence susceptibility in Semnan plain (Iran): a comparison of support vector machine and weights of evidence data mining algorithms," 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. 99(2), pages 951-971, November.
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    Cited by:

    1. Hüseyin Akay, 2022. "Towards Linking the Sustainable Development Goals and a Novel-Proposed Snow Avalanche Susceptibility Mapping," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(15), pages 6205-6222, December.

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