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Managing Rockfall Hazard on Strategic Linear Stakes: How Can Machine Learning Help to Better Predict Periods of Increased Rockfall Activity?

Author

Listed:
  • Marie-Aurélie Chanut

    (Team GéoCoD, Cerema, 69674 Bron, France)

  • Hermann Courteille

    (LISTIC, Université Savoie Mont-Blanc, 74944 Annecy, France)

  • Clara Lévy

    (BRGM, 45060 Orléans, France)

  • Abdourrahmane Atto

    (LISTIC, Université Savoie Mont-Blanc, 74944 Annecy, France)

  • Lucas Meignan

    (Géolithe, 38920 Crolles, France)

  • Emmanuel Trouvé

    (LISTIC, Université Savoie Mont-Blanc, 74944 Annecy, France)

  • Muriel Gasc-Barbier

    (Team GéoCoD, Cerema, 69674 Bron, France
    GERS, Université Gustave Eiffel, 77454 Marne-la-Vallée, France)

Abstract

When rockfalls hit and damage linear stakes such as roads or railways, the access to critical infrastructures (hospitals, schools, factories …) might be disturbed or stopped. Rockfall risk management often involves building protective structures that are traditionally based on the intensive use of resources such as steel or concrete. However, these solutions are expensive, considering their construction and maintenance, and it is very difficult to protect long linear stakes. A more sustainable and effective risk management strategy could be to account for changes on rockfall activity related to weather conditions. By integrating sustainability principles, we can implement mitigation measures that are less resource-intensive and more adaptable to environmental changes. For instance, instead of solely relying on physical barriers, solutions could include measures such as restriction of access, monitoring and mobilization of emergency kits containing eco-friendly materials. A critical step in developing such a strategy is accurately predicting periods of increased rockfall activity according to meteorological triggers. In this paper, we test four machine learning models to predict rockfalls on the National Road 1 at La Réunion, a key road for the socio-economic life of the island. Rainfall and rockfall data are used as inputs of the predictive models. We show that a set of features derived from the rainfall and rockfall data can predict rockfall with performances very close and almost slightly better than the standard expert model used for operational management. Metrics describing the performance of these models are translated in operational terms, such as road safety or the duration of road closings and openings, providing actionable insights for sustainable risk management practices.

Suggested Citation

  • Marie-Aurélie Chanut & Hermann Courteille & Clara Lévy & Abdourrahmane Atto & Lucas Meignan & Emmanuel Trouvé & Muriel Gasc-Barbier, 2024. "Managing Rockfall Hazard on Strategic Linear Stakes: How Can Machine Learning Help to Better Predict Periods of Increased Rockfall Activity?," Sustainability, MDPI, vol. 16(9), pages 1-30, April.
  • Handle: RePEc:gam:jsusta:v:16:y:2024:i:9:p:3802-:d:1387192
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