Robust storm surge forecasts for early warning system : a machine learning approach using Monte Carlo Bayesian model selection algorithm

Tools

MacDonald, E. and Tubaldi, E. and Patelli, E. (2025) Robust storm surge forecasts for early warning system : a machine learning approach using Monte Carlo Bayesian model selection algorithm. Stochastic Environmental Research and Risk Assessment. ISSN 1436-3240 (In Press) (https://doi.org/10.1007/s00477-025-02993-3)

[thumbnail of MacDonald-etal-SERRA-2025-Robust-storm-surge-forecasts-for-early-warning-system] Text. Filename: MacDonald-etal-SERRA-2025-Robust-storm-surge-forecasts-for-early-warning-system.pdf
Accepted Author Manuscript
Restricted to Repository staff only until 1 January 2099.
Download (2MB) | Request a copy

Abstract

Machine-learning based methods are increasingly employed for the prediction of storm surges and development of early warning systems for coastal flooding. The evaluation of the quality of such methods needs to explicitly consider the uncertainty of the prediction, which may stem from the inaccuracy in the forecasted inputs to the model as well as from the uncertainty inherent to the model itself. Defining the range of validity of the prediction is essential for the correct application of such models. A methodology is proposed for building a robust model for forecasting storm surges accounting for the relevant sources of uncertainty. The model uses as inputs the mean sea level pressure and wind velocity components at 10m above sea level. A set of Artificial Neural Networks are used in conjunction with an adaptive Bayesian model selection process to make robust storm surge forecast predictions with associated prediction intervals. The input uncertainty, characterised by comparing hindcast data and one day forecasted data, is propagated through the model via a Monte Carlo based approach. The application of the proposed methodology is illustrated by considering 24 hour target forecast predictions of storm surges for Millport, in the Firth of Clyde, Scotland, UK. It is shown that the proposed approach significantly improves the predictive performance of existing machine learning based models and provides a meaningful prediction interval that characterises feature, model and forecast uncertainty. The forecast system has negligible computational time requirements and showed very good agreement with observations achieving a CC of 0.942 for 24 hour forecasted surge from 2021 to 2023. The mean absolute error was 0.06m for all observations and only 0.10m for observations above 0.75m showing its accuracy for predicting extreme events.

ORCID iDs

MacDonald, E., Tubaldi, E. ORCID logoORCID: https://orcid.org/0000-0001-8565-8917 and Patelli, E. ORCID logoORCID: https://orcid.org/0000-0002-5007-7247;
CORE (COnnecting REpositories)