Monitoring water quality parameters in urban rivers using multi-source data and machine learning approach
文献类型：期刊论文
| 作者 | Liang, Yongchun1; Ding, Fangyu6; Liu, Lei1; Yin, Fang5; Hao, Mengmeng6; Kang, Tingting4; Zhao, Chuanpeng3; Wang, Ziteng2; Jiang, Dong6 |
| 刊名 | JOURNAL OF HYDROLOGY
 |
| 出版日期 | 2025-02-01 |
| 卷号 | 648页码:132394 |
| 关键词 | Urban rivers Water quality parameters Multispectral imagery Meteorological elements Land use Machine learning |
| ISSN号 | 0022-1694 |
| DOI | 10.1016/j.jhydrol.2024.132394 |
| 产权排序 | 2 |
| 文献子类 | Article |
| 英文摘要 | The systematic surveillance of nutrients and organic pollution in urban rivers is crucial for enhancing ecological integrity and promoting societal and economic sustainability. Currently, the primary methods of water quality monitoring involve on-site sampling and laboratory analysis, which are constrained by various factors such as terrain and climate. Remote sensing water quality monitoring, which enables large-scale, periodic, and comprehensive coverage, serves as an important supplement to these traditional methods. However, most current research on water quality monitoring predominantly relies on remote sensing technology, often overlooking the application of other multi-source data. In this study, we examined rivers in the Weihe River Basin by integrating field samples, Sentinel-2 multispectral imagery, meteorological elements, and land use types to construct machine learning (ML) models for predicting four water quality parameters (WQPs): ammonia nitrogen (NH3-N), total phosphorus (TP), chemical oxygen demand (COD), and dissolved oxygen (DO). The results showed that land use types significantly influenced the accuracy of predictions for NH3-N, TP, COD, and DO. Among the models evaluated, the Extra Tree Regression (ETR), eXtreme Gradient Boosting (XGBoost), and Gradient Boosting Regression (GBR) demonstrated the highest accuracy and transferability for monitoring WQPs in rivers. For instance, the models achieved the following coefficients of determination (R2) in 5-fold cross-validation: for NH3N, R2 was 0.65 in both the testing and validation datasets; for TP, R2 was 0.71 and 0.68; for COD, R2 was 0.50 and 0.47; and for DO, R2 was 0.68 and 0.64, respectively. Therefore, our findings underscore the feasibility of using multi-source data and ML methods to quantify water pollutants in urban rivers, providing essential technical support for monitoring the spatiotemporal dynamics of river water quality across extensive geographical areas. |
| URL标识 | 查看原文 |
| WOS关键词 | TOTAL PHOSPHORUS CONCENTRATION ; LAND-USE ; DISSOLVED-OXYGEN ; TOTAL NITROGEN ; URBANIZATION ; CHLOROPHYLL ; DEPLETION ; POLLUTION ; BODIES ; BLOOMS |
| WOS研究方向 | Engineering ; Geology ; Water Resources |
| 语种 | 英语 |
| WOS记录号 | WOS:001370608400001 |
| 出版者 | ELSEVIER |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/211289]  |
| 专题 | 资源利用与环境修复重点实验室_外文论文 |
| 通讯作者 | Jiang, Dong |
| 作者单位 | 1.Changan Univ, Sch Earth Sci & Resources, Xian 710054, Peoples R China; 2.Chinese Res Inst Environm Sci, State Key Lab Environm Criteria & Risk Assessment, Beijing 100012, Peoples R China 3.Chinese Acad Sci, Northeast Inst Geog & Agroecol, Key Lab Wetland Ecol & Environm, Changchun 130102, Peoples R China; 4.Peking Univ, Sch Urban Planning & Design, Shenzhen Grad Sch, Shenzhen 518055, Guangdong, Peoples R China; 5.Changan Univ, Sch Land Engn, Xian 710054, Peoples R China; 6.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China; |
| 推荐引用方式 GB/T 7714 | Liang, Yongchun,Ding, Fangyu,Liu, Lei,et al. Monitoring water quality parameters in urban rivers using multi-source data and machine learning approach[J]. JOURNAL OF HYDROLOGY,2025,648:132394. |
| APA | Liang, Yongchun.,Ding, Fangyu.,Liu, Lei.,Yin, Fang.,Hao, Mengmeng.,...&Jiang, Dong.(2025).Monitoring water quality parameters in urban rivers using multi-source data and machine learning approach.JOURNAL OF HYDROLOGY,648,132394. |
| MLA | Liang, Yongchun,et al."Monitoring water quality parameters in urban rivers using multi-source data and machine learning approach".JOURNAL OF HYDROLOGY 648(2025):132394. |
入库方式： OAI收割
来源：地理科学与资源研究所
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