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Erschienen in:
2024 | OriginalPaper | Buchkapitel
Spring Flow Prediction Model Based on VMD and Attention Mechanism LSTM
verfasst von : Jiayuan Wang, Baoju Zhang, Yonghong Hao, Bo Zhang, Cuiping Zhang, Cong Guo, Yuhao Zhu
Erschienen in: Communications, Signal Processing, and Systems
Verlag: Springer Nature Singapore
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Abstract
Spring flow prediction is the basis for water resources management, allocation, and effective utilization. To improve the accuracy of spring flow prediction, a hybrid model is used to predict, which combines variational modal decomposition (VMD), long and short-term memory (LSTM) network, and attention mechanism to overcome the endpoint effect and modal confounding problems of traditional empirical modal decomposition. This study explores the performance of VMD-LSTM-Attention and VMD-LSTM, LSTM models in spring water prediction. The experimental results confirm the effectiveness of VMD-LSTM-Attention in spring water prediction. Therefore, this hybrid model is robust and superior for predicting highly non-smooth and non-linear watersheds and can provide a reference for practical hydrological prediction.