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| Fuzzy comprehensive assessment of water quality and prediction of main pollutants in the Tuo River |
| Received:June 28, 2020 |
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| KeyWord:Tuo River;fuzzy comprehensive assessment;BP neural network |
| Author Name | Affiliation | E-mail | | FU Dong | School of Environment and Resource, Southwest University of Science and Technology, Mianyang 621010, China
School of Chemistry and Chemical Engineering, Sichuan University of Arts and Science, Dazhou 635000, China | | | WU Xue-fei | School of Environment and Resource, Southwest University of Science and Technology, Mianyang 621010, China | | | YI Zhen-yan | School of Environment and Resource, Southwest University of Science and Technology, Mianyang 621010, China | | | CHEN Yong-can | School of Environment and Resource, Southwest University of Science and Technology, Mianyang 621010, China
State Key Laboratory of Hydroscience and Engineering, Tsinghua University, Beijing 100084, China | chenyc@mail.tsinghua.edu.cn |
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| Abstract: |
| To accurately investigate the water quality of the Tuo River and to predict its main pollutants, the fuzzy comprehensive assessment model and the BP neural network were used, respectively. By selecting and optimizing the evaluation factors, a fuzzy comprehensive assessment of the water quality was conducted using the monthly water quality data of 31 monitored sections of the Tuo River from January 2018 to October 2019. Principal component analysis of water quality in the Tuo River was carried out to identify the main pollution sources and pollutants, and BP neural network was constructed to predict the main pollution factors. The results showed that the water quality of 9 sections of the Tuo River met Class Ⅰ water quality standards, and the remaining 22 sections were of Class Ⅴ water quality, and were distributed along the upper, middle, and lower reaches of the Tuo River. The concentration of TN exceeded Class Ⅳ water quality standards in all monitoring sections, of which 27 sections exceeded Class Ⅴ water quality standards. BP neural network constructed using the water quality data of the upstream section successfully predicted the TN concentration of the downstream section, with an average relative error of 2.041%. The results implied that the Tuo River was significantly polluted by TN, with non-point agricultural and industrial wastewater being the main sources of pollution. Additionally, according to this work, BP neural network of other sections of the Tuo River can be built to effectively predict the TN concentration in the Tuo River. Our findings can provide a reference for the comprehensive management and pollution control of the Tuo River basin. |
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