Shapelet-based decomposition stack machine learning model explains more middle river reaches water level hydrological process with high accuracy early warning
文献类型：期刊论文
| 作者 | Huan, Songhua1,2,3 |
| 刊名 | JOURNAL OF HYDROLOGY
 |
| 出版日期 | 2025-12-01 |
| 卷号 | 662页码:133927 |
| 关键词 | Data decomposition Hydrological process interpretation River water level Shapelet Stack machine learning model |
| ISSN号 | 0022-1694 |
| DOI | 10.1016/j.jhydrol.2025.133927 |
| 产权排序 | 1 |
| 文献子类 | Article |
| 英文摘要 | Flooding remains one of the most devastating natural hazards worldwide, yet understanding the complex hydrological processes that lead to flooding poses a significant challenge, hindering effective prevention efforts. To address this issue, this study proposes a stacked machine learning framework that integrates the Offline Shapelet Discovery (OSD) technique. Hydrological time series data are first decomposed using Empirical Wavelet Transform (EWT), and OSD is applied to generate a pool of potential shapelets for training. These shapelets are then processed using a deep learning model to produce preliminary predictions. Finally, an ensemble machine learning approach integrates these sub-predictions to generate the final forecast. The model is evaluated in the Pearl River Basin, a representative watershed encompassing several major urban areas. Compared with traditional machine learning methods, the proposed model demonstrates superior predictive performance across six stations located in the upper, middle and lower reaches of the basin. In the upper reaches, the model achieves a mean absolute error (MAE) of 0.2265, mean square error (MSE) of 0.0723, root mean square error (RMSE) of 0.2679, mean absolute percentage error (MAPE) of 0.0038, percent bias (PBIAS) of 0.0034 and Nash-Sutcliffe efficiency (NSE) of 0.8103. In the lower reaches, the respective values are 0.1766, 0.0619, 0.2720, 0.0415,-0.0007 and 0.8739, while in the middle reaches, they are 0.1239, 0.0362, 0.1890, 0.0059, 0.0007 and 0.9228. The shapelet pool reveals distinctive water level patterns, notably up-down-up-up and down-down-up-down types across various river segments. This study contributes to a deeper understanding of complex hydrological behaviors and provides new insights for enhancing flood prediction and prevention strategies through innovative data decomposition and pattern recognition techniques. |
| URL标识 | 查看原文 |
| WOS研究方向 | Engineering ; Geology ; Water Resources |
| 语种 | 英语 |
| WOS记录号 | WOS:001573660300001 |
| 出版者 | ELSEVIER |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/216114]  |
| 专题 | 陆地表层格局与模拟院重点实验室_外文论文 |
| 通讯作者 | Huan, Songhua |
| 作者单位 | 1.Univ Chinese Acad Sci, Beijing, Peoples R China; 2.Chinese Acad Sci, Acad Math & Syst Sci, Beijing, Peoples R China 3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing, Peoples R China; |
| 推荐引用方式 GB/T 7714 | Huan, Songhua. Shapelet-based decomposition stack machine learning model explains more middle river reaches water level hydrological process with high accuracy early warning[J]. JOURNAL OF HYDROLOGY,2025,662:133927. |
| APA | Huan, Songhua.(2025).Shapelet-based decomposition stack machine learning model explains more middle river reaches water level hydrological process with high accuracy early warning.JOURNAL OF HYDROLOGY,662,133927. |
| MLA | Huan, Songhua."Shapelet-based decomposition stack machine learning model explains more middle river reaches water level hydrological process with high accuracy early warning".JOURNAL OF HYDROLOGY 662(2025):133927. |
入库方式： OAI收割
来源：地理科学与资源研究所
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