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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
- Source :
- Water, Volume 13, Issue 17, Water, Vol 13, Iss 2406, p 2406 (2021)
- Publication Year :
- 2021
- Publisher :
- MDPI AG, 2021.
-
Abstract
- 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.
- Subjects :
- Chlorophyll a
South-to-North water transfer project
Project implementation
Geography, Planning and Development
North china
Aquatic Science
Machine learning
computer.software_genre
Biochemistry
Water scarcity
chemistry.chemical_compound
chlorophyll-a concentration prediction
TD201-500
Water Science and Technology
Trophic level
Water supply for domestic and industrial purposes
business.industry
random forest model
water quality management decision
support vector machine model
Hydraulic engineering
machine learning
Water transfer
chemistry
Environmental science
Water quality
Artificial intelligence
TC1-978
business
Eutrophication
computer
Subjects
Details
- ISSN :
- 20734441
- Volume :
- 13
- Database :
- OpenAIRE
- Journal :
- Water
- Accession number :
- edsair.doi.dedup.....3bf96739728b7e3ad40341944c2196a1