Storm Surge Modeling in the AI ERA: Using LSTM-based Machine Learning for Enhancing Forecasting Accuracy

arXiv:2403.04818v1 Announce Type: new
Abstract: Physics simulation results of natural processes usually do not fully capture the real world. This is caused for instance by limits in what physical processes are simulated and to what accuracy. In this work we propose and analyze the use of an LSTM-based deep learning network machine learning (ML) architecture for capturing and predicting the behavior of the systemic error for storm surge forecast models with respect to real-world water height observations from gauge stations …

abstract accuracy analyze arxiv cs.lg forecasting instance lstm machine machine learning modeling natural physics physics.ao-ph processes results simulation storm type work world