中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 地理科学与资源研究所
  3. 中国科学院地理科学与资源研究所
  4. 陆地水循环及地表过程重点实验室
  5. 外文论文
Water quality prediction based on sparse dataset using enhanced machine learning

文献类型：期刊论文

作者Huang, Sheng2,3,4; Xia, Jun1,3,4; Wang, Yueling1; Lei, Jiarui2; Wang, Gangsheng3,4
刊名ENVIRONMENTAL SCIENCE AND ECOTECHNOLOGY
出版日期2024-07-01
卷号20页码:100402
关键词Water quality modeling Sparse measurement River -lake confluence Long short-term memory Load estimator Machine learning
DOI10.1016/j.ese.2024.100402
产权排序4
文献子类Article
英文摘要Water quality in surface bodies remains a pressing issue worldwide. While some regions have rich water quality data, less attention is given to areas that lack sufficient data. Therefore, it is crucial to explore novel ways of managing source-oriented surface water pollution in scenarios with infrequent data collection such as weekly or monthly. Here we showed sparse-dataset-based prediction of water pollution using machine learning. We investigated the efficacy of a traditional Recurrent Neural Network alongside three Long Short-Term Memory (LSTM) models, integrated with the Load Estimator (LOADEST). The research was conducted at a river-lake confluence, an area with intricate hydrological patterns. We found that the Self-Attentive LSTM (SA-LSTM) model outperformed the other three machine learning models in predicting water quality, achieving Nash-Sutcliffe Efficiency (NSE) scores of 0.71 for CODMn and 0.57 for NH3N when utilizing LOADEST-augmented water quality data (referred to as the SA-LSTMLOADEST model). The SA-LSTM-LOADEST model improved upon the standalone SA-LSTM model by reducing the Root Mean Square Error (RMSE) by 24.6% for CODMn and 21.3% for NH3N. Furthermore, the model maintained its predictive accuracy when data collection intervals were extended from weekly to monthly. Additionally, the SA-LSTM-LOADEST model demonstrated the capability to forecast pollution loads up to ten days in advance. This study shows promise for improving water quality modeling in regions with limited monitoring capabilities. (c) 2024 The Authors. Published by Elsevier B.V. on behalf of Chinese Society for Environmental Sciences, Harbin Institute of Technology, Chinese Research Academy of Environmental Sciences. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
WOS关键词SOURCE POLLUTION PREDICTION ; BIDIRECTIONAL LSTM ; CHANGJIANG RIVER ; DONGTING LAKE ; POYANG LAKE ; PHOSPHORUS ; SEDIMENT ; NETWORK ; NITRATE ; CHINA
WOS研究方向Science & Technology - Other Topics ; Environmental Sciences & Ecology
WOS记录号WOS:001217567600001
源URL[http://ir.igsnrr.ac.cn/handle/311030/205201]  
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Xia, Jun; Wang, Yueling
作者单位1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Water Cycle & Related Land Surface Proc, Beijing 100101, Peoples R China
2.Natl Univ Singapore, Dept Civil & Environm Engn, Singapore 117578, Singapore
3.Wuhan Univ, Inst Water Carbon Cycles & Carbon Neutral, Wuhan 430072, Peoples R China
4.Wuhan Univ, State Key Lab Water Resources Engn & Management, Wuhan 430072, Peoples R China
推荐引用方式
GB/T 7714		 Huang, Sheng,Xia, Jun,Wang, Yueling,et al. Water quality prediction based on sparse dataset using enhanced machine learning[J]. ENVIRONMENTAL SCIENCE AND ECOTECHNOLOGY,2024,20:100402.
APA Huang, Sheng,Xia, Jun,Wang, Yueling,Lei, Jiarui,&Wang, Gangsheng.(2024).Water quality prediction based on sparse dataset using enhanced machine learning.ENVIRONMENTAL SCIENCE AND ECOTECHNOLOGY,20,100402.
MLA Huang, Sheng,et al."Water quality prediction based on sparse dataset using enhanced machine learning".ENVIRONMENTAL SCIENCE AND ECOTECHNOLOGY 20(2024):100402.

入库方式： OAI收割

来源：地理科学与资源研究所

浏览0
下载0
收藏0
其他版本

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。