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.
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Acknowledgements
This work was supported in part by the 2021 Tianjin Postgraduate Research and Innovation Project 2021YJSS209 and the 2022 Tianjin Normal University Graduate Research Innovation Project 2022KYCX104Y.
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Wang, J. et al. (2024). Spring Flow Prediction Model Based on VMD and Attention Mechanism LSTM. In: Wang, W., Liu, X., Na, Z., Zhang, B. (eds) Communications, Signal Processing, and Systems. CSPS 2023. Lecture Notes in Electrical Engineering, vol 1033. Springer, Singapore. https://doi.org/10.1007/978-981-99-7502-0_12
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DOI: https://doi.org/10.1007/978-981-99-7502-0_12
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