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<p>Flood forecasting is integral to water resources management and flood prevention. Recently, deep learning has made substantial progress in flood forecasting. However, neural network-based models require extensive datasets to ensure reliable precision. The scarcity of sufficient observed time-series data on flood events poses a major challenge to precise flood forecasting. Therefore, a modified Time-series Generative Adversarial Network (TimeGAN) is proposed to data enhancement and handle flood forecasting in this study. The proposed model uses the Transformer and Wasserstein distance loss function in the sequence generator, termed TW-TimeGAN, avoids gradient vanishing and exploding explosion, improving the reliability of model for long-term forecast. Meanwhile, the integrate of feature-temporal dual-attention with Recurrent Neural Network (RNN) in the recovery function enhances the capacity of TW-TimeGAN in extracting features. This paper utilized observed data and synthetic data to construct a flood prediction model by employing Long Short Term Memory (LSTM) and Bidirectional LSTM (BiLSTM). Through a comparative analysis of results, TW-TimeGAN achieves the lowest Dynamic Time Warping (DTW) (0.2332), demonstrating that TW-TimeGAN could effectively enhance the learning of precipitation and streamflow features, enabling the generation of high-quality synthetic flood sequences that closely resemble real flood events. Combined with flood prediction models, especially BiLSTM, the average Root Mean Square Error (RMSE) (7.67) and average Mean Absolute Error (MAE) (3.88) of the prediction results are the smallest, and the overall Nash-Sutcliffe Efficiency (NSE) (0.903) is the largest. It can be concluded that TW-TimeGAN-BiLSTM has the best prediction performance and demonstrates greater applicability in flood prediction.</p>

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A novel flood forecasting model based on TimeGAN for data-sparse basins

  • 期刊论文

  • 发表日期：

    2025年3月28日

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  • Chang Chen,
  • Fan Wang,
  • Zhongxiang Wang,
  • Dawei Zhang,
  • Liyun Xiang

摘要

Flood forecasting is integral to water resources management and flood prevention. Recently, deep learning has made substantial progress in flood forecasting. However, neural network-based models require extensive datasets to ensure reliable precision. The scarcity of sufficient observed time-series data on flood events poses a major challenge to precise flood forecasting. Therefore, a modified Time-series Generative Adversarial Network (TimeGAN) is proposed to data enhancement and handle flood forecasting in this study. The proposed model uses the Transformer and Wasserstein distance loss function in the sequence generator, termed TW-TimeGAN, avoids gradient vanishing and exploding explosion, improving the reliability of model for long-term forecast. Meanwhile, the integrate of feature-temporal dual-attention with Recurrent Neural Network (RNN) in the recovery function enhances the capacity of TW-TimeGAN in extracting features. This paper utilized observed data and synthetic data to construct a flood prediction model by employing Long Short Term Memory (LSTM) and Bidirectional LSTM (BiLSTM). Through a comparative analysis of results, TW-TimeGAN achieves the lowest Dynamic Time Warping (DTW) (0.2332), demonstrating that TW-TimeGAN could effectively enhance the learning of precipitation and streamflow features, enabling the generation of high-quality synthetic flood sequences that closely resemble real flood events. Combined with flood prediction models, especially BiLSTM, the average Root Mean Square Error (RMSE) (7.67) and average Mean Absolute Error (MAE) (3.88) of the prediction results are the smallest, and the overall Nash-Sutcliffe Efficiency (NSE) (0.903) is the largest. It can be concluded that TW-TimeGAN-BiLSTM has the best prediction performance and demonstrates greater applicability in flood prediction.