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A Data-Driven Multi-Step Flood Inundation Forecast System

Author

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  • Felix Schmid

    (Chair of Hydromechanics and Hydraulic Engineering, Research Institute Water and Environment, University of Siegen, 57076 Siegen, Germany)

  • Jorge Leandro

    (Chair of Hydromechanics and Hydraulic Engineering, Research Institute Water and Environment, University of Siegen, 57076 Siegen, Germany)

Abstract

Inundation maps that show water depths that occur in the event of a flood are essential for protection. Especially information on timings is crucial. Creating a dynamic inundation map with depth data in temporal resolution is a major challenge and is not possible with physical models, as these are too slow for real-time predictions. To provide a dynamic inundation map in real-time, we developed a data-driven multi-step inundation forecast system for fluvial flood events. The forecast system is based on a convolutional neural network (CNN), feature-informed dense layers, and a recursive connection from the predicted inundation at timestep t as a new input for timestep t + 1. The forecast system takes a hydrograph as input, cuts it at desired timesteps (t), and outputs the respective inundation for each timestep, concluding in a dynamic inundation map with a temporal resolution (t). The prediction shows a Critical Success Index (CSI) of over 90%, an average Root Mean Square Error (RMSE) of 0.07, 0.12, and 0.15 for the next 6 h, 12 h, and 24 h, respectively, and an individual RMSE value below 0.3 m, for all test datasets when compared with the results from a physically based model.

Suggested Citation

  • Felix Schmid & Jorge Leandro, 2024. "A Data-Driven Multi-Step Flood Inundation Forecast System," Forecasting, MDPI, vol. 6(3), pages 1-21, September.
    • Handle: RePEc:gam:jforec:v:6:y:2024:i:3:p:39-781:d:1477866
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    References listed on IDEAS

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    1. Nigel Arnell & Ben Lloyd-Hughes, 2014. "The global-scale impacts of climate change on water resources and flooding under new climate and socio-economic scenarios," Climatic Change, Springer, vol. 122(1), pages 127-140, January.
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