Preprints
https://doi.org/10.5194/hess-2022-83
https://doi.org/10.5194/hess-2022-83
 
02 Mar 2022
02 Mar 2022
Status: this preprint is currently under review for the journal HESS.

Deep Learning Methods for Flood Mapping: A Review of Existing Applications and Future Research Directions

Roberto Bentivoglio1, Elvin Isufi2, Sebastian Nicolaas Jonkman3, and Riccardo Taormina1 Roberto Bentivoglio et al.
  • 1Department of Water Management, Faculty of Civil Engineering and Geosciences, Delft University of Technology
  • 2Department of Intelligent Systems, Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology
  • 3Department of Hydraulic Engineering, Faculty of Civil Engineering and Geosciences, Delft University of Technology

Abstract. Deep Learning techniques have been increasingly used in flood management to overcome the limitations of accurate, yet slow, numerical models, and to improve the results of traditional methods for flood mapping. In this paper, we review 58 recent publications to outline the state-of-the-art of the field, identify knowledge gaps, and propose future research directions. The review focuses on the type of deep learning models used for various flood mapping applications, the flood types considered, the spatial scale of the studied events, and the data used for model development. The results show that models based on convolutional layers are usually more accurate as they leverage inductive biases to better process the spatial characteristics of the flooding events. Models based on fully-connected layers, instead, provide accurate results when coupled with other statistical models. Deep learning models showed increased accuracy when compared to traditional approaches and increased speed when compared to numerical methods. While there exist several applications in flood susceptibility, inundation, and hazard mapping, more work is needed to understand how deep learning can assist real-time flood warning during an emergency, and how it can be employed to estimate flood risk. A major challenge lies in developing deep learning models that can generalize to unseen case studies. Furthermore, all reviewed models and their outputs, are deterministic, with limited considerations for uncertainties in outcomes and probabilistic predictions. The authors argue that these identified gaps can be addressed by exploiting recent fundamental advancements in deep learning or by taking inspiration from developments in other applied areas. Models based on graph neural networks and neural operators can work with arbitrarily structured data and thus should be capable of generalizing across different case studies and could account for complex interactions with the natural and built environment. Physics-based deep learning can be used to preserve the underlying physical equations resulting in more reliable speed-up alternatives for numerical models. Similarly, probabilistic models can be built by resorting to Deep Gaussian Processes or Bayesian neural networks.

Roberto Bentivoglio et al.

Status: open (extended)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2022-83', Anonymous Referee #1, 05 Apr 2022 reply
    • AC1: 'Reply on RC1', Roberto Bentivoglio, 22 Apr 2022 reply
  • RC2: 'Comment on hess-2022-83', Anonymous Referee #2, 07 Apr 2022 reply
    • AC2: 'Reply on RC2', Roberto Bentivoglio, 22 Apr 2022 reply
      • RC3: 'Reply on AC2', Anonymous Referee #2, 22 Apr 2022 reply
  • RC4: 'Comment on hess-2022-83', Anonymous Referee #2, 22 Apr 2022 reply

Roberto Bentivoglio et al.

Roberto Bentivoglio et al.

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Short summary
Deep Learning methods have been increasingly used in flood management to improve traditional techniques. While promising results have been obtained, our review shows significant challenges in building Deep Lerning models that can i) generalize across multiple scenarios, ii) account for complex interactions, and iii) perform probabilistic predictions. We argue that these shortcomings could be addressed by transferring recent fundamental advancements in Deep Learning to flood mapping.