Which riverine water quality parameters can be predicted by meteorologically-driven deep learning?
文献类型：期刊论文
| 作者 | Huang, Sheng1; Wang, Yueling2; Xia, Jun1,2 |
| 刊名 | SCIENCE OF THE TOTAL ENVIRONMENT
 |
| 出版日期 | 2024-10-10 |
| 卷号 | 946页码:174357 |
| 关键词 | Water quality parameter Deep learning Meteorological condition Extreme value Climate change Collective prediction |
| DOI | 10.1016/j.scitotenv.2024.174357 |
| 产权排序 | 2 |
| 文献子类 | Article |
| 英文摘要 | River water quality has been significantly impacted by climate change and extreme weather events worldwide. Despite increasing studies on deep learning techniques for river water quality management, understanding which riverine water quality parameters can be well predicted by meteorologically-driven deep learning still requires further investigation. Here we explored the prediction performance of a traditional Recurrent Neural Network, a Long Short-Term Memory network (LSTM), and a Gated Recurrent Unit (GRU) using meteorological conditions as inputs in the Dahei River basin. We found that deep learning models (i.e., LSTM and GRU) demonstrated remarkable effectiveness in predicting multiple water quality parameters at daily scale, including water temperature, dissolved oxygen, electrical conductivity, chemical oxygen demand, ammonia nitrogen, total phosphorous, and total nitrogen, but not turbidity. The GRU model performed best with an average determination coefficient of 0.94. Compared to the daily-average prediction, the GRU model exhibited limited error increment of 10-40 % for most water quality parameters when predicting daily extreme values (i.e., the maximum and minimum). Moreover, deep learning showed superior performance in collective prediction for multiple water quality parameters than individual ones, enabling a more comprehensive understanding of the river water quality dynamics from meteorological data. This study holds the promise of applying meteorologically-driven deep learning techniques for water quality prediction to a broader range of watersheds, particularly in chemically ungauged areas. |
| WOS关键词 | SEDIMENT ; DYNAMICS ; RUNOFF ; MODEL |
| WOS研究方向 | Environmental Sciences & Ecology |
| WOS记录号 | WOS:001284326000001 |
| 出版者 | ELSEVIER |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/206875]  |
| 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 | Wang, Yueling; Xia, Jun |
| 作者单位 | 1.Wuhan Univ, State Key Lab Water Resources Engn & Management, Wuhan 430072, Peoples R China 2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Water Cycle & Related Land Surface Proc, Beijing 100101, Peoples R China |
| 推荐引用方式 GB/T 7714 | Huang, Sheng,Wang, Yueling,Xia, Jun. Which riverine water quality parameters can be predicted by meteorologically-driven deep learning?[J]. SCIENCE OF THE TOTAL ENVIRONMENT,2024,946:174357. |
| APA | Huang, Sheng,Wang, Yueling,&Xia, Jun.(2024).Which riverine water quality parameters can be predicted by meteorologically-driven deep learning?.SCIENCE OF THE TOTAL ENVIRONMENT,946,174357. |
| MLA | Huang, Sheng,et al."Which riverine water quality parameters can be predicted by meteorologically-driven deep learning?".SCIENCE OF THE TOTAL ENVIRONMENT 946(2024):174357. |
入库方式： OAI收割
来源：地理科学与资源研究所
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