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Deep learning models to predict flood events in fast-flowing watersheds.

Marco Luppichini1, Michele Barsanti2, Roberto Giannecchini3

  • 1Department of Earth Sciences, University of Study of Florence, Via La Pira 4, Florence, Italy; Department of Earth Sciences, University of Pisa, Via S. Maria, 52, 56126 Pisa, Italy.

The Science of the Total Environment
|November 26, 2021
PubMed
Summary

Deep learning models, specifically Long-Short Term Memory (LSTM) networks, show reliable flood warning forecast capabilities. These data-driven models are stable even with missing data, offering a viable alternative to physics-based models.

Keywords:
Arno RiverDeep learningFast catchment basinFlood forecastingHydraulic modelsLSTM

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Area of Science:

  • Hydrology and Water Resources
  • Artificial Intelligence in Environmental Science
  • Climate Change Adaptation

Background:

  • Traditional physics-based flood models often require extensive data and parameter estimation, limiting their application in data-scarce or complex small basin scenarios.
  • Accurate flood forecasting is crucial for effective territorial management, especially amidst increasing climatic changes.
  • The Arno River basin, with its densely populated cities and cultural heritage, presents a critical area for reliable flood prediction.

Purpose of the Study:

  • To evaluate the reliability of deep learning models, particularly Long-Short Term Memory (LSTM) networks, for flood warning forecasts.
  • To assess the applicability of these models for predicting short-duration flood events (lasting a few hours) using hydrometric data.
  • To establish a foundation for using data-driven models where physical modeling is challenging due to data limitations or basin complexity.

Main Methods:

  • Utilized Long-Short Term Memory (LSTM) deep learning architecture for flood event prediction.
  • Employed hydrometric control station data for forecasting flood events in the Arno River basin.
  • Tested model stability and robustness by simulating missing or erroneous input data.

Main Results:

  • Deep learning models demonstrated acceptable error margins for flood event identification with several hours' notice.
  • The LSTM models exhibited significant stability, performing well even when substantial amounts of input data were missing.
  • Achieved excellent results, comparable to physics-based models, with a less data-intensive approach.

Conclusions:

  • Deep learning, specifically LSTM, offers a reliable and robust method for flood warning systems, particularly in data-limited environments.
  • This data-driven approach provides a valuable alternative to physics-based models for flood prediction in complex or small river basins.
  • The study lays the groundwork for advanced hydrological forecasting, enhancing territorial management and climate change adaptation strategies.