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Deep learning forecast of rainfall-induced shallow landslides

Author

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  • Alessandro C. Mondini

    (Istituto di Ricerca per la Protezione Idrogeologica
    Istituto di Matematica Applicata e Tecnologie Informatiche “Enrico Magenes”)

  • Fausto Guzzetti

    (Istituto di Ricerca per la Protezione Idrogeologica
    Dipartimento della Protezione Civile)

  • Massimo Melillo

    (Istituto di Ricerca per la Protezione Idrogeologica)

Abstract

Rainfall triggered landslides occur in all mountain ranges posing threats to people and the environment. Given the projected climate changes, the risk posed by landslides is expected to increase, and the ability to anticipate their occurrence is key for effective risk reduction. Empirical thresholds and physically-based models are used to anticipate the short-term occurrence of rainfall-induced shallow landslides. But, evidence suggests that they may not be effective for operational forecasting over large areas. We propose a deep-learning based strategy to link rainfall to landslide occurrence. We inform and test the system with rainfall and landslide data available for the last 20 years in Italy. Our results indicate that it is possible to anticipate effectively the occurrence of rainfall-induced landslides over large areas, and that their location and timing are controlled primarily by the precipitation, opening to the possibility of operational landslide forecasting based on rainfall measurements and quantitative meteorological forecasts.

Suggested Citation

  • Alessandro C. Mondini & Fausto Guzzetti & Massimo Melillo, 2023. "Deep learning forecast of rainfall-induced shallow landslides," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
    • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-38135-y
    DOI: 10.1038/s41467-023-38135-y
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    References listed on IDEAS

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    1. Francesco Marra, 2019. "Rainfall thresholds for landslide occurrence: systematic underestimation using coarse temporal resolution data," 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. 95(3), pages 883-890, February.
    2. Stefano Luigi Gariano & Massimo Melillo & Silvia Peruccacci & Maria Teresa Brunetti, 2020. "How much does the rainfall temporal resolution affect rainfall thresholds for landslide triggering?," 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. 100(2), pages 655-670, January.
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    Cited by:

    1. Bruce Lambert & James Merten, 2024. "Standing Watch: Baselining Predictable Events That Influence Maritime Operations in the Context of the UN’s Sustainable Development Goals," Sustainability, MDPI, vol. 16(9), pages 1-26, May.
    2. Ascanio Rosi, 2023. "Exploring the Use of Pattern Classification Approaches for the Recognition of Landslide-Triggering Rainfalls," Sustainability, MDPI, vol. 15(20), pages 1-11, October.

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