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The use of machine learning techniques for a predictive model of debris flows triggered by short intense rainfall

Author

Listed:
  • M. Ponziani

    (CIMA Research Foundation)

  • D. Ponziani

    (CIMA Research Foundation)

  • A. Giorgi

    (Functional Centre of Aosta Valley)

  • H. Stevenin

    (Functional Centre of Aosta Valley)

  • S. M. Ratto

    (Functional Centre of Aosta Valley)

Abstract

The Alpine region of Aosta Valley has an early warning system to issue hydrogeological alerts up to 36 h in advance based on the output of hydrological models and rainfall thresholds. However, those thresholds generally do not apply to the debris flows triggered by local summer thunderstorms, which typically are intense rainfalls of short duration, with cumulative precipitation lower than 20 mm. Therefore, it is necessary to formulate a specific predictive debris-flow model, which takes into account other possible triggering factors. In this study, we have developed a predictive model for debris flows with machine learning techniques, using a detailed dataset composed by a variety of geomorphological and hydro-meteorological variables. The variables of the dataset were collected from daily measured and modelled data for all of the 91 drainage basins in which at least one debris-flow event was generated during the time period considered in this study (2009–2019). The performance of the model, using different machine learning techniques, was evaluated, and the most suitable model was chosen to be experimentally implemented in the existing early warning system of the region. The output of the model provides a debris-flow probability (DFP) for individual basins computed from the geomorphological and hydro-meteorological input variables.

Suggested Citation

  • M. Ponziani & D. Ponziani & A. Giorgi & H. Stevenin & S. M. Ratto, 2023. "The use of machine learning techniques for a predictive model of debris flows triggered by short intense rainfall," 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 143-162, May.
    • Handle: RePEc:spr:nathaz:v:117:y:2023:i:1:d:10.1007_s11069-023-05853-x
    DOI: 10.1007/s11069-023-05853-x
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    References listed on IDEAS

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    1. Xianyu Yu & Yi Wang & Ruiqing Niu & Youjian Hu, 2016. "A Combination of Geographically Weighted Regression, Particle Swarm Optimization and Support Vector Machine for Landslide Susceptibility Mapping: A Case Study at Wanzhou in the Three Gorges Area, Chin," IJERPH, MDPI, vol. 13(5), pages 1-35, May.
    2. Kuhn, Max, 2008. "Building Predictive Models in R Using the caret Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 28(i05).
    3. Mélanie Bertrand & Frédéric Liébault & Hervé Piégay, 2013. "Debris-flow susceptibility of upland catchments," 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. 67(2), pages 497-511, June.
    4. Michel Ponziani & Paolo Pogliotti & Hervé Stevenin & Sara Maria Ratto, 2020. "Debris-flow Indicator for an early warning system in the Aosta valley region," 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. 104(2), pages 1819-1839, November.
    5. Cheng Su & Lili Wang & Xizhi Wang & Zhicai Huang & Xiaocan Zhang, 2015. "Mapping of rainfall-induced landslide susceptibility in Wencheng, China, using support vector machine," 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. 76(3), pages 1759-1779, April.
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