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

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.

• Publication year

  2025

• Venue

  IEEE International Conference on Acoustics, Speech, and Signal Processing

• Publication date

  2025-04-06

• Fields of study

  Physics, Computer Science, Environmental Science

• Identifiers
  DOI 10.1109/ICASSP49660.2025.10887952
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

• DIFFormer: Scalable (Graph) Transformers Induced by Energy Constrained Diffusion

  2023cited by this paper
• Crossformer: Transformer Utilizing Cross-Dimension Dependency for Multivariate Time Series Forecasting

  2023cited by this paper
• Spatial-Temporal Identity: A Simple yet Effective Baseline for Multivariate Time Series Forecasting

  2022cited by this paper
• Modeling Oceanic Variables with Dynamic Graph Neural Networks

  2022cited by this paper
• Decoupled Dynamic Spatial-Temporal Graph Neural Network for Traffic Forecasting

  2022cited by this paper
• An advanced spatio-temporal convolutional recurrent neural network for storm surge predictions

  2022cited by this paper
• Prediction of estuarine water quality using interpretable machine learning approach

  2021cited by this paper
• Modeling Dynamic Processes of Mondego Estuary and Óbidos Lagoon Using Delft3D

  2021cited by this paper
• From Hydrometeorology to River Water Quality: Can a Deep Learning Model Predict Dissolved Oxygen at the Continental Scale?

  2021cited by this paper
• Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting

  2020cited by this paper
• Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks

  2020cited by this paper
• Impacts of Urbanization and Development on Estuarine Ecosystems and Water Quality

  2019cited by this paper
• Graph WaveNet for Deep Spatial-Temporal Graph Modeling

  2019cited by this paper
• Forecasting high-dimensional dynamics exploiting suboptimal embeddings

  2019cited by this paper
• Deep learning for multi-year ENSO forecasts

  2019cited by this paper
• COMPARISON BETWEEN MIKE 21 FM, DELFT3D AND DELFT3D FM FLOW MODELS OF WESTERN PORT BAY, AUSTRALIA

  2017cited by this paper
• Physical response of a back-barrier estuary to a post-tropical cyclone

  2017cited by this paper
• Human activities and climate variability drive fast‐paced change across the world's estuarine–coastal ecosystems

  2016cited by this paper
• MIKE SHE modeling of ecohydrological processes: Merits, applications, and challenges

  2016cited by this paper
• Hydrodynamic and sediment transport modeling of New River Inlet (NC) under the interaction of tides and waves

  2015cited by this paper
• Predicting the Storm Surge Threat of Hurricane Sandy with the National Weather Service SLOSH Model

  2014cited by this paper
• August 10–16, 2013

  2013cited by this paper
• Tropical cyclones and climate change

  2010cited by this paper
• The Laplacian on a Riemannian Manifold: An Introduction to Analysis on Manifolds

  1997cited by this paper
• Long Short-Term Memory

  1997cited by this paper

• No citing papers are available for this paper.