Abstract
Accurate prediction of monthly runoff is critical for optimal water resource allocation. However, previous studies mainly focused on the direct prediction of the decomposition sequence, ignoring the error accumulation and the increase in calculation time. In addition, the influence of each sequence on the prediction results was not clarified. Therefore, this study proposes a hybrid prediction method combining time varying filtering-based empirical mode decomposition (TVF-EMD), permutation entropy (PE), a long short-term memory model (LSTM) and a particle swarm algorithm (PSO). Firstly, TVF-EMD is applied for decomposing the original runoff sequences to obtain different components; secondly, PE is applied for characterizing the complexity of different components and reconstructing similar components to obtain new components; then, the decomposed-reconstructed runoff data are predicted by using the LSTM model with PSO based on the analytical studies of different watersheds. The outcomes indicate that the performance index of the proposed model is better than that of the comparison model, improving the prediction accuracy effectively. In addition, the impact of each subseries on prediction performance was also investigated in this study. These findings indicate that the developed model has potential application prospects in runoff prediction and can provide scientific support for water conservancy project operations.
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The data used in this study can be requested by the corresponding authors.
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Funding
National Natural Science Foundation of China, 51879291, Zhihe Chen.
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XY: data collection, conceptualization, Methodology, Writing—original draft. ZHC: Writing—review & editing, Funding acquisition. MQ: data collection, Writing—review & editing.
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Yang, X., Chen, Z. & Qin, M. Monthly Runoff Prediction Via Mode Decomposition-Recombination Technique. Water Resour Manage 38, 269–286 (2024). https://doi.org/10.1007/s11269-023-03668-w
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DOI: https://doi.org/10.1007/s11269-023-03668-w