Adaptive period decomposition network with muti-scale temporal variation learning for water level prediction
Study region:: Hanjiang River Basin, China Study focus:: This study introduces DLGNet, an advanced water level prediction model that employs an adaptive multi-resolution decomposition to decouple multiple periodic components. The model incorporates a 2D overlap patch attention mechanism and separabl...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2025-02-01
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| Series: | Journal of Hydrology: Regional Studies |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2214581824004695 |
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| _version_ | 1832591746338390016 |
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| author | Hui Wang LiPing Wang QiCang Qiu Yuyan Gao WenJuan Li QiuShun Wang |
| author_facet | Hui Wang LiPing Wang QiCang Qiu Yuyan Gao WenJuan Li QiuShun Wang |
| author_sort | Hui Wang |
| collection | DOAJ |
| description | Study region:: Hanjiang River Basin, China Study focus:: This study introduces DLGNet, an advanced water level prediction model that employs an adaptive multi-resolution decomposition to decouple multiple periodic components. The model incorporates a 2D overlap patch attention mechanism and separable dilation causal convolution to effectively capture local and global temporal variations. Additionally, DLGNet utilizes multiscale 2D separable convolutions to capture temporal variations among period components and employs a novel aggregation strategy for feature interaction analysis, ultimately enhancing prediction accuracy through direct decoding. New hydrologic insights for the region:: Compared to ten other advanced prediction models, DLGNet achieved an average reduction in mean square error (MSE) by 94. 82%, 93. 35%, and 83. 13% for short-term, mid-term, and long-term predictions, respectively. The adaptive period decomposition capability and multiscale temporal variation representation learning of DLGNet ensure its robustness and accuracy across various prediction tasks. Moreover, in all prediction tasks, the output of DLGNet more closely approximates the ground truth, exhibiting rich local detail changes and aligning more accurately with the actual fluctuations of water levels over time. |
| format | Article |
| id | doaj-art-0835eff9ea0f42b6891819728a7b2322 |
| institution | Kabale University |
| issn | 2214-5818 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Hydrology: Regional Studies |
| spelling | doaj-art-0835eff9ea0f42b6891819728a7b23222025-01-22T05:42:06ZengElsevierJournal of Hydrology: Regional Studies2214-58182025-02-0157102120Adaptive period decomposition network with muti-scale temporal variation learning for water level predictionHui Wang0LiPing Wang1QiCang Qiu2Yuyan Gao3WenJuan Li4QiuShun Wang5Computer Science & Technology, Zhejiang University of Technology, HangZhou, ChinaComputer Science & Technology, Zhejiang University of Technology, HangZhou, China; Corresponding author.Zhejiang Lab, No. 1818, Western Road of Wenyi, Hangzhou, Zhejiang, ChinaComputer Science & Technology, Zhejiang University of Technology, HangZhou, ChinaMeteorological Bureau of Zhejiang Province, HangZhou, ChinaZhejiang Institute of Hydraulics and Estuary, HangZhou, ChinaStudy region:: Hanjiang River Basin, China Study focus:: This study introduces DLGNet, an advanced water level prediction model that employs an adaptive multi-resolution decomposition to decouple multiple periodic components. The model incorporates a 2D overlap patch attention mechanism and separable dilation causal convolution to effectively capture local and global temporal variations. Additionally, DLGNet utilizes multiscale 2D separable convolutions to capture temporal variations among period components and employs a novel aggregation strategy for feature interaction analysis, ultimately enhancing prediction accuracy through direct decoding. New hydrologic insights for the region:: Compared to ten other advanced prediction models, DLGNet achieved an average reduction in mean square error (MSE) by 94. 82%, 93. 35%, and 83. 13% for short-term, mid-term, and long-term predictions, respectively. The adaptive period decomposition capability and multiscale temporal variation representation learning of DLGNet ensure its robustness and accuracy across various prediction tasks. Moreover, in all prediction tasks, the output of DLGNet more closely approximates the ground truth, exhibiting rich local detail changes and aligning more accurately with the actual fluctuations of water levels over time.http://www.sciencedirect.com/science/article/pii/S2214581824004695Time seriesWater level predictionDeep learningPattern recognition |
| spellingShingle | Hui Wang LiPing Wang QiCang Qiu Yuyan Gao WenJuan Li QiuShun Wang Adaptive period decomposition network with muti-scale temporal variation learning for water level prediction Journal of Hydrology: Regional Studies Time series Water level prediction Deep learning Pattern recognition |
| title | Adaptive period decomposition network with muti-scale temporal variation learning for water level prediction |
| title_full | Adaptive period decomposition network with muti-scale temporal variation learning for water level prediction |
| title_fullStr | Adaptive period decomposition network with muti-scale temporal variation learning for water level prediction |
| title_full_unstemmed | Adaptive period decomposition network with muti-scale temporal variation learning for water level prediction |
| title_short | Adaptive period decomposition network with muti-scale temporal variation learning for water level prediction |
| title_sort | adaptive period decomposition network with muti scale temporal variation learning for water level prediction |
| topic | Time series Water level prediction Deep learning Pattern recognition |
| url | http://www.sciencedirect.com/science/article/pii/S2214581824004695 |
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