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<p>The evolution of hydrological processes was highly nonlinear due to climate change, human activities, and changes in the watershed subsurface. Typical deep learning (DL) schemes still had limitations in terms of accuracy, stability, and computational complexity. In this study, a novel design based on the “encoding-decoding” architecture was adopted, drawing upon recent advances in the field of artificial intelligence. Incorporating a wavelet feature extractor, the Transformer encoder module was retained to encode the features of the input sequences, while a new module, TimesBlock, was introduced to realize runoff prediction. A new model called WaveTransTimesNet (WTTN) was proposed. The experimental results demonstrated that WTTN exhibited superior prediction performance, excellent generalization ability, and strong robustness, with the Kling-Gupta Efficiency coefficient (KGE) reaching 0.94, 0.95, and 0.96, respectively. Compared to other benchmark models, the proposed model not only identified and utilized periodic trend features in the runoff series more effectively but also predicted runoff more accurately. Additionally, it performed exceptionally well in peak prediction.</p>

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WaveTransTimesNet: an enhanced deep learning monthly runoff prediction model based on wavelet transform and transformer architecture

  • 期刊论文

  • 发表日期：

    2025年1月24日

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  • Dong-mei Xu,
  • Zong Li,
  • Wen-chuan Wang,
  • Yang-hao Hong,
  • Miao Gu,
  • Xiao-xue Hu,
  • Jun Wang

摘要

The evolution of hydrological processes was highly nonlinear due to climate change, human activities, and changes in the watershed subsurface. Typical deep learning (DL) schemes still had limitations in terms of accuracy, stability, and computational complexity. In this study, a novel design based on the “encoding-decoding” architecture was adopted, drawing upon recent advances in the field of artificial intelligence. Incorporating a wavelet feature extractor, the Transformer encoder module was retained to encode the features of the input sequences, while a new module, TimesBlock, was introduced to realize runoff prediction. A new model called WaveTransTimesNet (WTTN) was proposed. The experimental results demonstrated that WTTN exhibited superior prediction performance, excellent generalization ability, and strong robustness, with the Kling-Gupta Efficiency coefficient (KGE) reaching 0.94, 0.95, and 0.96, respectively. Compared to other benchmark models, the proposed model not only identified and utilized periodic trend features in the runoff series more effectively but also predicted runoff more accurately. Additionally, it performed exceptionally well in peak prediction.