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Hybrid Surrogate Model for Timely Prediction of Flash Flood Inundation Maps Caused by Rapid River Overflow

Author

Listed:
  • Andre D. L. Zanchetta

    (Department of Civil Engineering, McMaster University, 1280 Main Street West, Hamilton, ON L8S 4L7, Canada)

  • Paulin Coulibaly

    (Department of Civil Engineering, McMaster University, 1280 Main Street West, Hamilton, ON L8S 4L7, Canada
    School of Earth, Environment and Society, McMaster University, 1280 Main Street West, Hamilton, ON L8S 4L7, Canada
    Institute for Water, Environment, and Health, United Nations University, 175 Longwood Road, Hamilton, ON L8P 0A1, Canada)

Abstract

Timely generation of accurate and reliable forecasts of flash flood events is of paramount importance for flood early warning systems in urban areas. Although physically based models are able to provide realistic reproductions of fast-developing inundation maps in high resolutions, the high computational demand of such hydraulic models makes them difficult to be implemented as part of real-time forecasting systems. This paper evaluates the use of a hybrid machine learning approach as a surrogate of a quasi-2D urban flood inundation model developed in PCSWMM for an urban catchment located in Toronto (Ontario, Canada). The capability to replicate the behavior of the hydraulic model was evaluated through multiple performance metrics considering error, bias, correlation, and contingency table analysis. Results indicate that the surrogate system can provide useful forecasts for decision makers by rapidly generating future flood inundation maps comparable to the simulations of physically based models. The experimental tool developed can issue reliable alerts of upcoming inundation depths on traffic locations within one to two hours of lead time, which is sufficient for the adoption of important preventive actions. These promising outcomes were achieved in a deterministic setup and use only past records of precipitation and discharge as input during runtime.

Suggested Citation

  • Andre D. L. Zanchetta & Paulin Coulibaly, 2022. "Hybrid Surrogate Model for Timely Prediction of Flash Flood Inundation Maps Caused by Rapid River Overflow," Forecasting, MDPI, vol. 4(1), pages 1-23, January.
    • Handle: RePEc:gam:jforec:v:4:y:2022:i:1:p:7-148:d:731299
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    References listed on IDEAS

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    1. Jascha Lehmann & Dim Coumou & Katja Frieler, 2015. "Erratum to: increased record-breaking precipitation events under global warming," Climatic Change, Springer, vol. 132(4), pages 517-518, October.
    2. Jascha Lehmann & Dim Coumou & Katja Frieler, 2015. "Increased record-breaking precipitation events under global warming," Climatic Change, Springer, vol. 132(4), pages 501-515, October.
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    Cited by:

    1. Mariusz Starzec & Sabina Kordana-Obuch & Daniel Słyś, 2023. "Assessment of the Feasibility of Implementing a Flash Flood Early Warning System in a Small Catchment Area," Sustainability, MDPI, vol. 15(10), pages 1-43, May.

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