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Exploring a similarity search-based data-driven framework for multi-step-ahead flood forecasting.

Kangling Lin1, Hua Chen1, Yanlai Zhou1

  • 1State Key Laboratory of Water Resources Engineering and Management, Wuhan University, Wuhan 430072, China.

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Summary
This summary is machine-generated.

This study introduces a novel Similarity search-based Temporal Convolutional Network Encoder-Decoder (S-TCNED) model for accurate, multi-step flood forecasting. The S-TCNED model enhances reliability for large floods, even in extreme conditions, by learning from similar historical events.

Keywords:
Encoder-Decoder (ED)Flood forecastRainfall-runoff relationshipSimilarity searchTemporal convolutional network (TCN)

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Area of Science:

  • Hydrology and Water Resources
  • Artificial Intelligence in Environmental Science
  • Data-Driven Predictive Modeling

Background:

  • Accurate flood forecasting, especially for large events and extended horizons, remains a challenge for artificial neural networks due to limited data.
  • Existing models struggle when forecast horizons exceed river basin concentration times.

Purpose of the Study:

  • To propose and evaluate a novel Similarity search-based data-driven framework for multi-step-ahead flood forecasting.
  • To enhance the reliability and accuracy of flood forecasts for large floods and extended forecast horizons.
  • To compare the performance of the proposed model against a conventional Temporal Convolutional Network Encoder-Decoder (TCNED) model.

Main Methods:

  • Developed a Similarity search-based Temporal Convolutional Network Encoder-Decoder (S-TCNED) model.
  • Utilized 5232 hourly hydrological data points for training and testing.
  • Input sequences included historical flood flows and rainfall data from 15 stations; output sequences predicted 1- to 16-hour-ahead flood forecasts.
  • Compared S-TCNED with a conventional TCNED model.

Main Results:

  • Both TCNED and S-TCNED models provided suitable multi-step-ahead flood forecasts.
  • S-TCNED demonstrated superior performance in mimicking long-term rainfall-runoff relationships.
  • S-TCNED offered more reliable and accurate forecasts for large floods compared to TCNED, particularly under extreme weather conditions.
  • A significant positive correlation was observed between improved sample label density and Nash-Sutcliffe Efficiency (NSE) for S-TCNED at longer forecast horizons (13-16 hours).

Conclusions:

  • The S-TCNED model effectively enhances flood forecast reliability and accuracy by learning from similar historical flood development processes.
  • The similarity search mechanism allows targeted learning, improving performance especially for long forecast horizons.
  • The proposed framework is effective in extending the length of reliable flood forecast horizons.