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Forecasting of compound ocean-fluvial floods using machine learning.

Sogol Moradian1, Amir AghaKouchak2, Salem Gharbia3

  • 1College of Science and Engineering, University of Galway, Galway, Ireland; EHIRG EcoHydroInformatics Research Group, University of Galway, Ireland.

Journal of Environmental Management
|June 14, 2024
PubMed
Summary

This study introduces a novel hydrodynamic-machine learning approach for forecasting compound coastal-fluvial floods. The system accurately predicts flood inundation and water depths, outperforming traditional methods for early warning systems.

Keywords:
Artificial intelligenceCompound hazardsFlood predictionFluvial-ocean floodingMachine learning

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Area of Science:

  • Environmental Science
  • Hydrology
  • Data Science

Background:

  • Conventional flood assessments often overlook compound events driven by multiple sources (ocean, fluvial, pluvial).
  • Effective flood risk management requires accurate forecasting of complex, multi-driver flood events.

Purpose of the Study:

  • To develop and evaluate a novel two-step framework for modelling and forecasting compound coastal-fluvial floods.
  • To integrate hydrodynamic simulations with machine learning for enhanced flood prediction accuracy and speed.

Main Methods:

  • A two-step framework combining hydrodynamic simulation for flood propagation and machine learning (ML) models for forecasting.
  • Seven ML models (SVR, SVM, RBF, LR, GPR, DT, ANN) were trained per pixel using river discharge and ocean water level data.
  • The system was applied and validated for compound coastal-fluvial flood forecasting in Cork City, Ireland.

Main Results:

  • The coupled hydrodynamic-ML approach provides reliable estimates of flood inundation and water depths.
  • The Radial Basis Function (RBF) model demonstrated the best performance among the tested ML models.
  • The system successfully forecasts coastal-fluvial floods with limited input data, overcoming computational limitations of traditional models.

Conclusions:

  • The developed system offers a computationally efficient alternative to traditional hydrodynamic models for short-term flood forecasting.
  • This approach enables near real-time flood forecasting, suitable for integration into early warning systems.
  • Accurate compound flood forecasting has significant societal benefits, including improved preparedness and reduced flood damage.