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Article

Machine Learning-Based Prediction of Chlorophyll-a Variations in Receiving Reservoir of World’s Largest Water Transfer Project—A Case Study in the Miyun Reservoir, North China

by 1,2, 3, 1,2,*, 2 and 2
1
State Key Laboratory of Water Environment Simulation, School of Environment, Beijing Normal University, Beijing 100875, China
2
Key Laboratory for Water and Sediment Sciences of Ministry of Education, School of Environment, Beijing Normal University, Beijing 100875, China
3
Chinese Academy for Environmental Planning, Beijing 100012, China
*
Author to whom correspondence should be addressed.
Academic Editor: José Gutiérrez-Pérez
Water 2021, 13(17), 2406; https://doi.org/10.3390/w13172406
Received: 24 July 2021 / Revised: 25 August 2021 / Accepted: 30 August 2021 / Published: 1 September 2021
Although water transfer projects can alleviate the water crisis, they may cause potential risks to water quality safety in receiving areas. The Miyun Reservoir in northern China, one of the receiving reservoirs of the world’s largest water transfer project (South-to-North Water Transfer Project, SNWTP), was selected as a case study. Considering its potential eutrophication trend, two machine learning models, i.e., the support vector machine (SVM) model and the random forest (RF) model, were built to investigate the trophic state by predicting the variations of chlorophyll-a (Chl-a) concentrations, the typical reflection of eutrophication, in the reservoir after the implementation of SNWTP. The results showed that compared with the SVM model, the RF model had higher prediction accuracy and more robust prediction ability with abnormal data, and was thus more suitable for predicting Chl-a concentration variations in the receiving reservoir. Additionally, short-term water transfer would not cause significant variations of Chl-a concentrations. After the project implementation, the impact of transferred water on the water quality of the receiving reservoir would have gradually increased. After a 10-year implementation, transferred water would cause a significant decline in the receiving reservoir’s water quality, and Chl-a concentrations would increase, especially from July to August. This led to a potential risk of trophic state change in the Miyun Reservoir and required further attention from managers. This study can provide prediction techniques and advice on water quality security management associated with eutrophication risks resulting from water transfer projects. View Full-Text
Keywords: chlorophyll-a concentration prediction; machine learning; support vector machine model; random forest model; water quality management decision; South-to-North water transfer project chlorophyll-a concentration prediction; machine learning; support vector machine model; random forest model; water quality management decision; South-to-North water transfer project
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MDPI and ACS Style

Liao, Z.; Zang, N.; Wang, X.; Li, C.; Liu, Q. Machine Learning-Based Prediction of Chlorophyll-a Variations in Receiving Reservoir of World’s Largest Water Transfer Project—A Case Study in the Miyun Reservoir, North China. Water 2021, 13, 2406. https://doi.org/10.3390/w13172406

AMA Style

Liao Z, Zang N, Wang X, Li C, Liu Q. Machine Learning-Based Prediction of Chlorophyll-a Variations in Receiving Reservoir of World’s Largest Water Transfer Project—A Case Study in the Miyun Reservoir, North China. Water. 2021; 13(17):2406. https://doi.org/10.3390/w13172406

Chicago/Turabian Style

Liao, Zhenmei, Nan Zang, Xuan Wang, Chunhui Li, and Qiang Liu. 2021. "Machine Learning-Based Prediction of Chlorophyll-a Variations in Receiving Reservoir of World’s Largest Water Transfer Project—A Case Study in the Miyun Reservoir, North China" Water 13, no. 17: 2406. https://doi.org/10.3390/w13172406

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