Ensemble learning using multivariate variational mode decomposition based on the Transformer for multi-step-ahead streamflow forecasting
Fang, Jinjie, Yang, Linshan, Wen, Xiaohu, Yu, Haijiao, Li, Weide, Adamowski, Jan F. and Barzegar, Rahim, (2024). Ensemble learning using multivariate variational mode decomposition based on the Transformer for multi-step-ahead streamflow forecasting. Journal of Hydrology, 636 131275-n/a
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Article
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Sub-type Journal article Author Fang, Jinjie
Yang, Linshan
Wen, Xiaohu
Yu, Haijiao
Li, Weide
Adamowski, Jan F.
Barzegar, RahimTitle Ensemble learning using multivariate variational mode decomposition based on the Transformer for multi-step-ahead streamflow forecasting Appearing in Journal of Hydrology Volume 636 Publication Date 2024-04-11 Place of Publication Amesterdam Publisher Elsevier B.V. Start page 131275 End page n/a Language eng Abstract Accurate streamflow forecasting is critical in the domain of water resources management. However, the inherently non-stationary and stochastic nature of streamflow poses a significant challenge to achieving accuracy in streamflow forecasting. In this study, we introduce an MVMD-ensembled Transformer model (MVMD-Transformer), which incorporates the MVMD for concurrent time–frequency analysis of streamflow and related potential influencing variables. The model aligns common modes in the decomposition results, ensuring that the different variables corresponding to each mode have the same center frequency. This alignment overcomes frequency mismatches and helps uncover the intrinsic patterns and essential features between streamflow and associated variables. During the forecasting phase, the Transformer component of the MVMD-Transformer model establishes connections among streamflow and other influencing variables across pairs of nodes in each mode. We tested the performance of the MVMD-Transformer model in forec Copyright Holder Elsevier B. V. Copyright Year 2024 Copyright type All rights reserved DOI 10.1016/j.jhydrol.2024.131275 -
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