Managing Rockfall Hazard on Strategic Linear Stakes: How Can Machine Learning Help to Better Predict Periods of Increased Rockfall Activity? - Centre d'études et d'expertise sur les risques, l'environnement, la mobilité et l'aménagement Access content directly
Journal Articles Sustainability Year : 2024

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

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.
Fichier principal
Vignette du fichier
sustainability-16-03802-v2.pdf (57.63 Mo) Télécharger le fichier
Origin Publisher files allowed on an open archive

Dates and versions

hal-04573900 , version 1 (13-05-2024)

Identifiers

Cite

Marie-Aurélie Chanut, Hermann Courteille, Clara Lévy, Abdourrahmane Atto, Lucas Meignan, et al.. Managing Rockfall Hazard on Strategic Linear Stakes: How Can Machine Learning Help to Better Predict Periods of Increased Rockfall Activity?. Sustainability, 2024, 16 (9), pp.3802. ⟨10.3390/su16093802⟩. ⟨hal-04573900⟩
0 View
0 Download

Altmetric

Share

Gmail Mastodon Facebook X LinkedIn More