Mansong Xi
Research Subjects
Research Interests
Mansong Xi has a total of 8 co-authors. Here are the top co-authors by number of shared publications:
- Decai Liu
National Observation and Research Station of Erhai Lake Ecosystem in Yunnan, Dali 671000, China
1 shared publication - Jian Shen
School of Environmental Science and Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China; National Observation and Research Station of Erhai Lake Ecosystem in Yunnan, Dali, 671000, China; Yunnan Dali Research Institute of Shanghai Jiao Tong University, Dali, 671000, China
1 shared publication - Jimeng Feng
Shanghai Jiao Tong University Yunnan Dali Research Institute, Dali 671000, China
1 shared publication - Rui Li
Yunnan Institute of Water & Hydropower Engineering Investigation, Design and Research, Kunming 650233, China
1 shared publication - Tianyang Sun
National Observation and Research Station of Erhai Lake Ecosystem in Yunnan, Dali 671000, China
1 shared publication - Xinze Wang
School of Environmental Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China; National Observation and Research Station of Erhai Lake Ecosystem in Yunnan, Dali 671000, China; Yunnan Dali Research Institute of Shanghai Jiao Tong University, Dali 671000, China
1 shared publication
Latest Publications
- Open Access
The Estimation of Chemical Oxygen Demand of Erhai Lake Basin and Its Links with DOM Fluorescent Components Using Machine Learning
Water quality estimation tools based on real-time monitoring are essential for the effective management of organic pollution in watersheds. This study aims to monitor changes in the levels of chemical oxygen demand (COD, CODMn) and dissolved organic matter (DOM) in Erhai Lake Basin, exploring their relationships and the ability of DOM to estimate COD and CODMn. Excitation emission matrix–parallel factor analysis (EEM–PARAFAC) of DOM identified protein-like component (C1) and humic-like components (C2, C3, C4). Combined with random forest (RF), maximum fluorescence intensity (Fmax) values of components were selected as estimation parameters to establish models. Results proved that the COD of rivers was more sensitive to the reduction in C1 and C2, while CODMn was more sensitive to C4. The DOM of Erhai Lake thrived by internal sources, and the relationship between COD, CODMn, and DOM of Erhai Lake was more complicated than rivers (inflow rivers of Erhai Lake). Models for rivers achieved good estimations, and by adding dissolved oxygen and water temperature, the estimation ability of COD models for Erhai Lake was significantly improved. This study demonstrates that DOM-based machine learning can be used as an alternative tool for real-time monitoring of organic pollution and deepening the understanding of the relationship between COD, CODMn, and DOM, and provide a scientific basis for water quality management.
Open AccessThe Estimation of Chemical Oxygen Demand of Erhai Lake Basin and Its Links with DOM Fluorescent Components Using Machine Learning
Water quality estimation tools based on real-time monitoring are essential for the effective management of organic pollution in watersheds. This study aims to monitor changes in the levels of chemical oxygen demand (COD, CODMn) and dissolved organic matter (DOM) in Erhai Lake Basin, exploring their relationships and the ability of DOM to estimate COD and CODMn. Excitation emission matrix–parallel factor analysis (EEM–PARAFAC) of DOM identified protein-like component (C1) and humic-like components (C2, C3, C4). Combined with random forest (RF), maximum fluorescence intensity (Fmax) values of components were selected as estimation parameters to establish models. Results proved that the COD of rivers was more sensitive to the reduction in C1 and C2, while CODMn was more sensitive to C4. The DOM of Erhai Lake thrived by internal sources, and the relationship between COD, CODMn, and DOM of Erhai Lake was more complicated than rivers (inflow rivers of Erhai Lake). Models for rivers achieved good estimations, and by adding dissolved oxygen and water temperature, the estimation ability of COD models for Erhai Lake was significantly improved. This study demonstrates that DOM-based machine learning can be used as an alternative tool for real-time monitoring of organic pollution and deepening the understanding of the relationship between COD, CODMn, and DOM, and provide a scientific basis for water quality management.