Storm surge dynamics in the northern Adriatic Sea: comparing AI emulators with high-resolution numerical simulations

Abstract

Accurate storm surge forecasting is vital for protecting coastal regions, particularly in the northern Adriatic Sea where sea-level rise and increasingly severe storm events pose growing risks. Machine Learning (ML) approaches offer compelling speed and flexibility, yet their ability to emulate high-resolution dynamic models, especially for extreme surge events, has not been sufficiently assessed across methods and loss functions. In this study, a range of ML emulators, from Multivariate Linear Regression (MLR) to Long Short-Term Memory (LSTM) networks, is benchmarked against a high-resolution hydrodynamic model optimized for extreme surge representation. We also evaluate the impact of training loss functions, comparing the conventional Mean Squared Error (MSE) with the corrected Mean Absolute Deviation squared (MADc2), designed to better capture surge peaks. Results show that even simple models like MLR, when trained with MADc2, achieve performance comparable to advanced neural networks while remaining orders of magnitude faster. These findings demonstrate that with appropriate training strategies, data-driven emulators can rival physics-based models in reproducing extremes. Simple models such as MLR and the Multilayer Perceptron (MLP), when trained with MADc2, demonstrate a practical balance between computational efficiency and accuracy, underscoring the potential of ML emulators for coastal forecasting and risk assessment.