Forecasting of compound ocean-fluvial floods using machine learning

Sogol Moradian; Amir AghaKouchak; Salem Gharbia; Ciaran Broderick; Agnieszka I Olbert

doi:10.1016/j.jenvman.2024.121295

Forecasting of compound ocean-fluvial floods using machine learning

J Environ Manage. 2024 Jul:364:121295. doi: 10.1016/j.jenvman.2024.121295. Epub 2024 Jun 13.

Authors

Sogol Moradian¹, Amir AghaKouchak², Salem Gharbia³, Ciaran Broderick⁴, Agnieszka I Olbert⁵

Affiliations

¹ College of Science and Engineering, University of Galway, Galway, Ireland; EHIRG EcoHydroInformatics Research Group, University of Galway, Ireland.
² Department of Civil and Environmental Engineering, University of California, Irvine, CA, USA; Department of Earth System Science, University of California, Irvine, CA, USA.
³ Department of Environmental Science, Atlantic Technological University, Sligo, Ireland.
⁴ Flood Forecasting Division, Met Éireann, Dublin, Ireland.
⁵ College of Science and Engineering, University of Galway, Galway, Ireland; EHIRG EcoHydroInformatics Research Group, University of Galway, Ireland; Ryan Institute for Environmental, Marine and Energy Research, University of Galway, Galway, Ireland; MaREI Research Centre for Energy, Climate and Marine, University of Galway, Galway, Ireland. Electronic address: indiana.olbert@nuigalway.ie.

PMID: 38875991
DOI: 10.1016/j.jenvman.2024.121295

Abstract

Flood modelling and forecasting can enhance our understanding of flood mechanisms and facilitate effective management of flood risk. Conventional flood hazard and risk assessments usually consider one driver at a time, whether it is ocean, fluvial or pluvial, without considering the compound nature of flood events. In this paper, we developed a novel approach for modelling and forecasting compound coastal-fluvial floods using a two-step framework. In step one, a hydrodynamic model is used to simulate floodwater propagation; while in step two, machine learning (ML) models are used to generate flood forecasts. The architecture of hydrodynamic-ML forecasting system incorporates a hydrodynamic model covering a specific domain, with individual ML models trained for each pixel. In total 7 ML models including: Support Vector Regression (SVR), Support Vector Machine (SVM), Radial Basis Function (RBF), Linear Regression (LR), Gaussian Process Regression (GPR), Decision Tree (DT), and Artificial Neural Network (ANN) were applied in this study. Forecasting compound floods is achieved using two sets of inputs: timeseries of river discharges in the upstream fluvial section and downstream ocean water levels in the coastal areas. The accuracy of the flood forecasting system is demonstrated for Cork City, Ireland; and modelling performance was evaluated using several statistical tools. Results show that the proposed models can provide reliable estimates of flood inundation and associated water depths. Overall, the RBF model exhibits the best performance. Despite the complexity of compound multi-driver floods, this study shows that the coupled hydrodynamic-ML approach can forecast coastal-fluvial flood with limited hydraulic and hydrological input data. This system overcomes the limitations of traditional hydrodynamic model-based systems where trade-offs between the always competing numerical model accuracy and computational time prohibit the model to be used for short-term flood forecasting. Once trained, the ML component of the coupled system can perform flood forecasting in near real-time, potentially integrating into a flood early warning system. Accurate flood forecasting has a wide range of positive societal impacts, including improved flood preparedness, increased confidence, better resource allocation, reduced flood damage, and potentially even flood prevention.

Keywords: Artificial intelligence; Compound hazards; Flood prediction; Fluvial-ocean flooding; Machine learning.

MeSH terms

Floods*
Forecasting*
Machine Learning*
Models, Theoretical
Neural Networks, Computer
Oceans and Seas
Rivers
Support Vector Machine*