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

Marie-Aurélie Chanut (), Hermann Courteille, Clara Lévy, Abdourrahmane Atto, Lucas Meignan, Emmanuel Trouvé and Muriel Gasc-Barbier
Additional contact information
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

Sustainability, 2024, vol. 16, issue 9, 1-30

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.

Keywords: rockfall; rainfall; machine learning; predictive models; risk management; transient mitigation measures (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/16/9/3802/pdf (application/pdf)
https://www.mdpi.com/2071-1050/16/9/3802/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:16:y:2024:i:9:p:3802-:d:1387192

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().