Water Quality Evaluation of the Yangtze River in China Using Machine Learning Techniques and Data Monitoring on Different Time Scales
| Autor: | Zhenzhen Di, Miao Chang, Peikun Guo |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Pollution
Watershed lcsh:Hydraulic engineering media_common.quotation_subject expectation-maximization clustering Geography Planning and Development watershed management Aquatic Science Machine learning computer.software_genre Biochemistry water quality lcsh:Water supply for domestic and industrial purposes monitoring indicators lcsh:TC1-978 Water environment real-time data Permanganate index Water Science and Technology media_common Pollutant lcsh:TD201-500 business.industry Watershed management Environmental science Artificial intelligence Water quality business computer Surface water hierarchical clustering |
| Zdroj: | Water, Vol 11, Iss 2, p 339 (2019) Water Volume 11 Issue 2 |
| ISSN: | 2073-4441 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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