TIDE-Net: A Physics-Based Graph Model for Predicting Tropical Cyclone Impacts on Estuarine Systems

Abstract

Estuarine systems, located at the interface of land, ocean, and atmosphere, are vital to global ecosystems and economies due to their rich exchanges among multiple environments. Tropical cyclones cause significant fluctuations in salinity and other environmental parameters within estuaries, impacting their resilience. Accurately predicting these changes aids in decision-making to protect these ecosystems. While graph-based deep learning models have shown promise, they often fail to capture the complex interdependencies between observation stations. To address this, we propose an energy-constrained diffusion feature representation that synergizes with ocean dynamics. Using data from 24 observation points in the Yangtze River Estuary during three tropical cyclones, our method effectively predicts and interprets dynamic environmental changes, offering robust support for protecting estuarine systems.