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Open AccessArticle

Water Quality Evaluation of the Yangtze River in China Using Machine Learning Techniques and Data Monitoring on Different Time Scales

School of Environment, Tsinghua University, Beijing 100084, China
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Water 2019, 11(2), 339; https://doi.org/10.3390/w11020339
Received: 26 December 2018 / Revised: 10 February 2019 / Accepted: 12 February 2019 / Published: 16 February 2019
(This article belongs to the Section Water Resources Management and Governance)
Unlike developed countries, China has a nationally unified water environment standard and a specific watershed protection bureau to perform water quality evaluation. It is a major challenge to assess the water quality of a large watershed at a wide spatial scale and to make decisions in a scientific way. In 2016, weekly and real-time data for four monitoring indicators (pH, dissolved oxygen, permanganate index, and ammonia nitrogen) were collected at 21 surface water sections (sites) of the Yangtze River Basin, China. Results showed that one site had a relatively low Site Water Quality Index and was polluted for 12 weeks meanwhile. By using expectation-maximization clustering and hierarchical clustering algorithms, the 21 sites were classified. Variable spatiotemporal distribution characteristics for water quality and pollutants were found; some sites exhibited similar water quality variations on the weekly scale, but had different yearly grades. The results revealed polluted water quality for short periods and abrupt anomalies, which imply potential pollution sources and negative effects on water ecosystems. Potential spatio-temporal water quality characteristics, explored by machine learning methods and evidenced by time series and statistical models, could be applied in environmental decision support systems to make watershed management more objective, reliable, and powerful. View Full-Text
Keywords: water quality; real-time data; monitoring indicators; expectation-maximization clustering; hierarchical clustering; watershed management water quality; real-time data; monitoring indicators; expectation-maximization clustering; hierarchical clustering; watershed management
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MDPI and ACS Style

Di, Z.; Chang, M.; Guo, P. Water Quality Evaluation of the Yangtze River in China Using Machine Learning Techniques and Data Monitoring on Different Time Scales. Water 2019, 11, 339.

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