Multi-Source Data Fusion and Heuristic-Optimized Machine Learning for Large-Scale River Water Quality Parameters Monitoring
文献类型：期刊论文
| 作者 | Fang, Kehang3,5; Wu, Feng2,4,5; Gao, Xing1,5; Li, Zhihui2,4,5 |
| 刊名 | REMOTE SENSING
 |
| 出版日期 | 2026-01-18 |
| 卷号 | 18期号:2页码:320 |
| 关键词 | multi-source data fusion water quality inversion machine learning optimization whale optimization algorithm SHAP interpretability |
| DOI | 10.3390/rs18020320 |
| 产权排序 | 1 |
| 文献子类 | Article |
| 英文摘要 | Highlights What are the main findings? Fused satellite, meteorology, land use, and landscape data can enhance the accuracy of water quality remote sensing inversion. Introduced WOA to auto-optimize ML models, enhancing inversion accuracy and stability. What are the implications of the main findings? Extended water quality inversion from urban to large-scale basins with diverse drivers. Integrated landscape pattern indices into models to reveal spatial pollution mechanisms.Highlights What are the main findings? Fused satellite, meteorology, land use, and landscape data can enhance the accuracy of water quality remote sensing inversion. Introduced WOA to auto-optimize ML models, enhancing inversion accuracy and stability. What are the implications of the main findings? Extended water quality inversion from urban to large-scale basins with diverse drivers. Integrated landscape pattern indices into models to reveal spatial pollution mechanisms.Abstract Accurate and efficient surface water quality monitoring is essential for ecological protection and sustainable development. However, conventional monitoring methods, such as fixed-site observations, often suffer from spatial limitations and overlook crucial auxiliary variables. This study proposes an innovative modeling framework for large-scale river water quality inversion that integrates multi-source data-including Sentinel-2 imagery, meteorological conditions, land use classification, and landscape pattern indices. To improve predictive accuracy, three tree-based machine learning models (Random Forest, XGBoost, and LightGBM) were constructed and further optimized using the Whale Optimization Algorithm (WOA), a nature-inspired metaheuristic technique. Additionally, model interpretability was enhanced using SHAP (Shapley Additive Explanations), enabling a transparent understanding of each variable's contribution. The framework was applied to the Red River Basin (RRB) to predict six key water quality parameters: dissolved oxygen (DO), ammonia nitrogen (NH3-N), total phosphorus (TP), total nitrogen (TN), pH, and permanganate index (CODMn). Results demonstrate that integrating landscape and meteorological variables significantly improves model performance compared to remote sensing alone. The best-performing models achieved R2 values exceeding 0.45 for all parameters (DO: 0.70, NH3-N: 0.46, TP: 0.59, TN: 0.71, pH: 0.83, CODMn: 0.57). Among them, WOA-optimized LightGBM consistently delivered superior performance. The study also confirms the feasibility of applying the models across the entire basin, offering a transferable and interpretable approach to spatiotemporal water quality prediction in other large-scale or data-scarce regions. |
| URL标识 | 查看原文 |
| WOS关键词 | IMPACTS |
| WOS研究方向 | Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:001671491700001 |
| 出版者 | MDPI |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/221034]  |
| 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 | Li, Zhihui |
| 作者单位 | 1.Chinese Acad Sci, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China 2.Chinese Acad Sci, Key Lab Land Surface Pattern & Simulat, Beijing 100101, Peoples R China; 3.Donghua Univ, Coll Math & Stat, Shanghai 200051, Peoples R China; 4.Univ Chinese Acad Sci, Beijing 100049, Peoples R China; 5.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China; |
| 推荐引用方式 GB/T 7714 | Fang, Kehang,Wu, Feng,Gao, Xing,et al. Multi-Source Data Fusion and Heuristic-Optimized Machine Learning for Large-Scale River Water Quality Parameters Monitoring[J]. REMOTE SENSING,2026,18(2):320. |
| APA | Fang, Kehang,Wu, Feng,Gao, Xing,&Li, Zhihui.(2026).Multi-Source Data Fusion and Heuristic-Optimized Machine Learning for Large-Scale River Water Quality Parameters Monitoring.REMOTE SENSING,18(2),320. |
| MLA | Fang, Kehang,et al."Multi-Source Data Fusion and Heuristic-Optimized Machine Learning for Large-Scale River Water Quality Parameters Monitoring".REMOTE SENSING 18.2(2026):320. |
入库方式： OAI收割
来源：地理科学与资源研究所
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