Addressing multi-scale temporal variability: deep integration and application of the CNN and transformer model in monthly streamflow prediction
文献类型：期刊论文
| 作者 | Fan, Jinsheng4; Yu, Guo-An3; Zhao, Mingmeng4; Zong, Hucheng1,2 |
| 刊名 | EXPERT SYSTEMS WITH APPLICATIONS
 |
| 出版日期 | 2025-11-01 |
| 卷号 | 292页码:128658 |
| 关键词 | Monthly streamflow prediction VMD CNN Transformer CovTransformer |
| ISSN号 | 0957-4174 |
| DOI | 10.1016/j.eswa.2025.128658 |
| 产权排序 | 2 |
| 文献子类 | Article |
| 英文摘要 | Accurate monthly streamflow prediction is essential for effective water resource management, hydropower operation, and ecological sustainability. However, streamflow processes are inherently nonlinear and exhibit considerable multiscale temporal variability, driven by both natural conditions and potential anthropogenic influences. To address these challenges, we propose a novel hybrid deep learning model, ISVM-CovTransformer, which integrates the Improved Sparrow Search Algorithm (ISSA), Variational Mode Decomposition (VMD), Mutual Information (MI), and a composite CovTransformer architecture. Within this framework, ISSA is utilized to optimize the parameters of VMD for efficient signal decomposition, while MI is employed to identify informative input features with strong predictive relevance. The CovTransformer model, combining Convolutional Neural Networks (CNN) and Transformer layers, enables the simultaneous extraction of localized temporal patterns and long-range dependencies, thereby enhancing the model's ability to capture complex runoff dynamics and improve prediction accuracy. Using monthly precipitation and streamflow data from the Tangnaihai, Toudaoguai, and Huayuankou hydrological stations, experimental results demonstrate that the proposed model outperforms baseline approaches. Specifically, during the testing phase, the model achieved an NSC of 0.9686, RMSE of 91.99 m3/s, MAE of 70.90 m3/s, R2 of 0.9702, and a PBIAS of-1.198 % at Tangnaihai; an NSC of 0.9498, RMSE of 90.35 m3/s, MAE of 724.63 m3/s, R2 of 0.9554, and a PBIAS of 2.573 % at Toudaoguai; and an NSC of 0.9302, RMSE of 174.36 m3/s, MAE of 44.42 m3/s, R2 of 0.9393, and a PBIAS of 3.309 % at Huayuankou. These findings confirm the proposed model's effectiveness for monthly streamflow forecasting and suggest that it provides a theoretically sound and generalizable framework, with potential extensions to related hydrological applications such as sediment transport modeling. |
| URL标识 | 查看原文 |
| WOS关键词 | FRAMEWORK |
| WOS研究方向 | Computer Science ; Engineering ; Operations Research & Management Science |
| 语种 | 英语 |
| WOS记录号 | WOS:001517352900018 |
| 出版者 | PERGAMON-ELSEVIER SCIENCE LTD |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/215351]  |
| 专题 | 陆地水循环及地表过程院重点实验室_外文论文 |
| 通讯作者 | Fan, Jinsheng; Yu, Guo-An |
| 作者单位 | 1.Minist Water Resources, Key Lab Water Management & Water Secur Yellow Rive, Zhengzhou 450003, Peoples R China 2.Yellow River Engn Consulting Co Ltd, Postdoctoral Programme, Zhengzhou 450003, Peoples R China; 3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Water Cycle & Related Land Surface Proc, 11A Datun Rd, Beijing 100101, Peoples R China; 4.Zhoukou Normal Univ, Zhoukou 466001, Peoples R China; |
| 推荐引用方式 GB/T 7714 | Fan, Jinsheng,Yu, Guo-An,Zhao, Mingmeng,et al. Addressing multi-scale temporal variability: deep integration and application of the CNN and transformer model in monthly streamflow prediction[J]. EXPERT SYSTEMS WITH APPLICATIONS,2025,292:128658. |
| APA | Fan, Jinsheng,Yu, Guo-An,Zhao, Mingmeng,&Zong, Hucheng.(2025).Addressing multi-scale temporal variability: deep integration and application of the CNN and transformer model in monthly streamflow prediction.EXPERT SYSTEMS WITH APPLICATIONS,292,128658. |
| MLA | Fan, Jinsheng,et al."Addressing multi-scale temporal variability: deep integration and application of the CNN and transformer model in monthly streamflow prediction".EXPERT SYSTEMS WITH APPLICATIONS 292(2025):128658. |
入库方式： OAI收割
来源：地理科学与资源研究所
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