Affiliation:
1. a Water Conservancy College, North China University of Water Resources and Electric Power, Zhengzhou 450046, China
2. b Collaborative Innovation Center of Water Resources Efficient Utilization and Protection Engineering, Zhengzhou 450046, China
3. c Technology Research Center of Water Conservancy and Marine Traffic Engineering, Zhengzhou, Henan Province 450046, China
Abstract
Abstract
Accurate prediction of monthly precipitation is crucial for effective regional water resources management and utilization. However, precipitation series are influenced by multiple factors, exhibiting significant ambiguity, chance, and uncertainty. In this research, we propose a combined model that integrates adaptive noise-complete ensemble empirical mode decomposition (CEEMDAN), variational modal decomposition method (VMD), and bidirectional long- and short-term memory (BILSTM) to enhance precipitation prediction. We apply this model to forecast precipitation in Fuzhou City and compare its performance with existing models, including CEEMD–long and short-term memory (LSTM), CEEMD–BILSTM, and CEEMDAN–BILSTM. Our findings demonstrate that the combined CEEMDAN–VMD–BILSTM quadratic decomposition model yields more accurate predictions and captures the real variation in precipitation series with greater fidelity. The model achieves an average relative error of 1.69%, at a lower level, and an average absolute error of 1.32 m, with a Nash–Sutcliffe efficiency coefficient of 0.92. Overall, the proposed quadratic decomposition model exhibits excellent applicability, stability, and superior predictive capabilities in monthly precipitation forecasting.
Funder
Key Scientific Research Project of Colleges and Universities in Henan Province
Subject
Water Science and Technology
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