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Watershed Planning within a Quantitative Scenario Analysis Framework
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A physically informed domain-independent data-driven inundation forecast model.

Felix Schmid1, Leonie Müller1, Jorge Leandro1

  • 1Department of Civil Engineering, Chair of Hydromechanics and Hydraulic Engineering, Research Institute Water and Environment, University of Siegen, 57076 Siegen, Germany.

Water Research
|October 19, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a novel physically informed data-driven model for real-time pluvial flood inundation forecasting. The system provides accurate spatial and temporal water depth predictions in new areas, improving upon traditional methods.

Keywords:
Continuity & kinematic wave lossDeep learningGeneralization to unseen catchmentsPhysics-informed neural networkSpatio-temporal predictionUrban flooding

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Area of Science:

  • Hydrology and Water Resources Engineering
  • Geospatial Analysis
  • Artificial Intelligence in Environmental Science

Background:

  • Operational flood forecasting requires accurate spatial and temporal water depth data, crucial for public safety during pluvial flood events.
  • Traditional physically-based models are too slow for real-time predictions, while existing data-driven models lack domain independence.
  • Current data-driven approaches often require downsampling for larger catchments, limiting their applicability.

Purpose of the Study:

  • To develop a physically informed data-driven forecast system capable of real-time spatial and temporal water depth inundation predictions in previously unseen areas.
  • To overcome the limitations of traditional and existing data-driven flood forecasting models.
  • To enhance the domain independence and applicability of data-driven flood models.

Main Methods:

  • A Convolutional Neural Network (CNN) employing an image-to-image translation process was developed, trained on catchment characteristics from Baiersdorf, Germany.
  • A spatiotemporal prediction framework was implemented, featuring 10-minute time-stepping and domain-independent forecasting tested across 23 unknown areas.
  • A physically informed loss function was integrated, incorporating a 2D continuity equation and kinematic wave formulation to estimate velocity and enforce physical constraints.

Main Results:

  • The model achieved a Critical Success Index (CSI) of approximately 74% and a mean Root Mean Squared Error (RMSE) of 0.045 m on unknown areas.
  • The physically informed loss function demonstrated superior performance compared to a standard data-driven loss function, reducing RMSE by about 25%.
  • The framework successfully provided forecasts with 10-minute temporal resolution, eliminating the need for downsampling.

Conclusions:

  • The proposed physically informed data-driven system offers a viable solution for operational, real-time pluvial flood inundation mapping in diverse geographical locations.
  • Integrating physical principles into data-driven models significantly enhances prediction accuracy and reliability for flood forecasting.
  • The developed framework demonstrates the potential for accurate and efficient flood prediction systems applicable to a wide range of catchments.