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Toward the probabilistic forecasting of cyclone-induced marine flooding by overtopping at Reunion Island aided by a time-varying random-forest classification approach

Abstract : In 2017, Irma and Maria highlighted the vulnerability of small islands to cyclonic events and the necessity of advancing the forecast techniques for cyclone-induced marine flooding. In this context, this paper presents a generic approach to deriving time-varying inundation forecasts from ensemble track and intensity forecasts applied to the case of Reunion Island in the Indian Ocean. The challenge for volcanic islands is to account for the full complexity of wave overtopping processes while also ensuring a robustness and timeliness that are compatible with emergency requirements. The challenge is addressed by following a hybrid approach relying on the combination of process-based models with a statistical model (herein, a random-forest classifier) trained with a precalculated database. The latter enables one to translate any time series of coastal marine conditions into the time-varying probability of inundation for different sectors. The application detailed for the case of Cyclone Dumile at Sainte-Suzanne city shows that the proposed approach enables quick discrimination, in both space and time, thereby identifying safe and exposed areas and demonstrating that probabilistic forecasting of marine flooding by overtopping is feasible. The whole method can be easily adapted to other territories and scales provided that validated process-based models are available. Beyond early warning applications, the developed database and statistical models may also be used for informing risk prevention and adaptation strategies.
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https://hal-brgm.archives-ouvertes.fr/hal-03742115
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Soumis le : mardi 2 août 2022 - 12:36:32
Dernière modification le : mercredi 3 août 2022 - 04:06:21

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Sophie Lecacheux, Jérémy Rohmer, F. Paris, Rodrigo Pedreros, H. Quetelard, et al.. Toward the probabilistic forecasting of cyclone-induced marine flooding by overtopping at Reunion Island aided by a time-varying random-forest classification approach. Natural Hazards, Springer Verlag, 2021, 105 (1), pp.227-251. ⟨10.1007/s11069-020-04307-y⟩. ⟨hal-03742115⟩

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