Impact of High Temporal Resolution Data on Water Quality Modeling: Insights from Erhai Case Study
文献类型：期刊论文
| 作者 | Shi, Xiaomeng3,4; Li, Yu3,4 ; Yao B(姚波)2; Wang, Shengrui3,4; Ni, Shouqing1
|
| 刊名 | PROCESSES
 |
| 出版日期 | 2025-05-31 |
| 卷号 | 13期号:6页码:22 |
| 关键词 | high-frequency monitoring water quality prediction temporal resolution sensitivity machine learning wavelet analysis |
| DOI | 10.3390/pr13061726 |
| 通讯作者 | Li, Yu(yu.li@bnu.edu.cn) |
| 英文摘要 | Lake monitoring is essential for sustaining aquatic ecosystems, and accurate estimation/prediction of water quality parameters is crucial to this effort. Despite its importance, the performance of predictive models built on varying temporal resolutions remains underexplored systematically. This study used daily and 4 h high temporal resolution (HTR) datasets to assess the performance of multiple machine learning models-namely, Support Vector Regression (SVR), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Long Short-Term Memory (LSTM) networks-under consistent data scales. The results indicate that dissolved oxygen (DO) exhibits pronounced sensitivity to temporal resolution, while total nitrogen (TN), total phosphorus (TP), and ammonia nitrogen (NH3-N) show distinct, parameter-specific response patterns that align with the temporal characteristics of their underlying biogeochemical processes. This research helps to deepen the understanding of how temporal data resolution influences model performance in water quality prediction, offering valuable insights for selecting optimal data resolutions and modeling techniques to enhance lake monitoring and protection strategies. |
| 分类号 | Q3 |
| WOS关键词 | LAKE |
| 资助项目 | Key R&D Program of Shandong Province ; Major Science and Technology Project of Yunnan Province[202202AE090034] ; Project of Science and Technology Department of Yunnan Province[202304BQ040005] ; Project of Science and Technology Department of Yunnan Province[202305AF150055] ; Interdisciplinary Intelligence SuperComputer Center of Beijing Normal University Zhuhai ; [2021CXGC011202] |
| WOS研究方向 | Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:001514925800001 |
| 资助机构 | Key R&D Program of Shandong Province ; Major Science and Technology Project of Yunnan Province ; Project of Science and Technology Department of Yunnan Province ; Interdisciplinary Intelligence SuperComputer Center of Beijing Normal University Zhuhai |
| 其他责任者 | Li, Yu |
| 源URL | [http://dspace.imech.ac.cn/handle/311007/101883]  |
| 专题 | 力学研究所_流固耦合系统力学重点实验室(2012-) |
| 作者单位 | 1.Shandong Univ, Sch Environm Sci & Engn, Jinan 250100, Peoples R China 2.Chinese Acad Sci, Key Lab Mech Fluid Solid Coupling Syst, Inst Mech, Beijing 100190, Peoples R China; 3.Beijing Normal Univ Zhuhai, Adv Inst Nat Sci, Ctr Water Res, Guangdong Hong Kong Joint Lab Water Secur, Zhuhai 519087, Peoples R China; 4.Beijing Normal Univ, Coll Water Sci, Beijing 100875, Peoples R China; |
| 推荐引用方式 GB/T 7714 | Shi, Xiaomeng,Li, Yu,Yao B,et al. Impact of High Temporal Resolution Data on Water Quality Modeling: Insights from Erhai Case Study[J]. PROCESSES,2025,13(6):22. |
| APA | Shi, Xiaomeng,Li, Yu,姚波,Wang, Shengrui,&Ni, Shouqing.(2025).Impact of High Temporal Resolution Data on Water Quality Modeling: Insights from Erhai Case Study.PROCESSES,13(6),22. |
| MLA | Shi, Xiaomeng,et al."Impact of High Temporal Resolution Data on Water Quality Modeling: Insights from Erhai Case Study".PROCESSES 13.6(2025):22. |
入库方式： OAI收割
来源：力学研究所
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