Machine Learning-based Water Quality Forecasting for Shenzhen Bay
Based on high-frequency monitoring data collected by the buoy online monitoring system in Shenzhen Bay, machine learning methods including artificial neural networks (ANN), support vector regression (SVR), and random forest (RF) are employed to conduct short-term forecasting of water quality paramet...
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| Format: | Article |
| Language: | zho |
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Editorial Office of Pearl River
2024-07-01
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| Series: | Renmin Zhujiang |
| Subjects: | |
| Online Access: | http://www.renminzhujiang.cn/thesisDetails#10.3969/j.issn.1001-9235.2024.07.002 |
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| author | XIONG Jianzhi XIONG Rui LU Haiyan ZHENG Yi |
| author_facet | XIONG Jianzhi XIONG Rui LU Haiyan ZHENG Yi |
| author_sort | XIONG Jianzhi |
| collection | DOAJ |
| description | Based on high-frequency monitoring data collected by the buoy online monitoring system in Shenzhen Bay, machine learning methods including artificial neural networks (ANN), support vector regression (SVR), and random forest (RF) are employed to conduct short-term forecasting of water quality parameters such as dissolved oxygen (DO), chlorophyll-a (Chl.a), total nitrogen (TN), and total phosphorus (TP). The research findings indicate that utilizing high-frequency in-situ water quality monitoring data enables accurate prediction of water quality in Shenzhen Bay within 24 hours. Specifically, ANN is found to be the most suitable for forecasting DO, Chl.a, and TN, with nash-sutcliffe efficiency (NSE) values greater than 0.60 for the 24-hour forecast period. Meanwhile, the RF model is found to be the most suitable for TP forecasting, with NSE values greater than 0.76 within 24 hours. The findings of this study have important implications for the precise prevention and control of water pollution in the Guangdong-Hong Kong-Macao Greater Bay Area. |
| format | Article |
| id | doaj-art-f4c639d773104a3291a4ea61301052f5 |
| institution | Kabale University |
| issn | 1001-9235 |
| language | zho |
| publishDate | 2024-07-01 |
| publisher | Editorial Office of Pearl River |
| record_format | Article |
| series | Renmin Zhujiang |
| spelling | doaj-art-f4c639d773104a3291a4ea61301052f52025-01-15T03:01:15ZzhoEditorial Office of Pearl RiverRenmin Zhujiang1001-92352024-07-0145101866690810Machine Learning-based Water Quality Forecasting for Shenzhen BayXIONG JianzhiXIONG RuiLU HaiyanZHENG YiBased on high-frequency monitoring data collected by the buoy online monitoring system in Shenzhen Bay, machine learning methods including artificial neural networks (ANN), support vector regression (SVR), and random forest (RF) are employed to conduct short-term forecasting of water quality parameters such as dissolved oxygen (DO), chlorophyll-a (Chl.a), total nitrogen (TN), and total phosphorus (TP). The research findings indicate that utilizing high-frequency in-situ water quality monitoring data enables accurate prediction of water quality in Shenzhen Bay within 24 hours. Specifically, ANN is found to be the most suitable for forecasting DO, Chl.a, and TN, with nash-sutcliffe efficiency (NSE) values greater than 0.60 for the 24-hour forecast period. Meanwhile, the RF model is found to be the most suitable for TP forecasting, with NSE values greater than 0.76 within 24 hours. The findings of this study have important implications for the precise prevention and control of water pollution in the Guangdong-Hong Kong-Macao Greater Bay Area.http://www.renminzhujiang.cn/thesisDetails#10.3969/j.issn.1001-9235.2024.07.002water quality forecastingmachine learningShenzhen Bay |
| spellingShingle | XIONG Jianzhi XIONG Rui LU Haiyan ZHENG Yi Machine Learning-based Water Quality Forecasting for Shenzhen Bay Renmin Zhujiang water quality forecasting machine learning Shenzhen Bay |
| title | Machine Learning-based Water Quality Forecasting for Shenzhen Bay |
| title_full | Machine Learning-based Water Quality Forecasting for Shenzhen Bay |
| title_fullStr | Machine Learning-based Water Quality Forecasting for Shenzhen Bay |
| title_full_unstemmed | Machine Learning-based Water Quality Forecasting for Shenzhen Bay |
| title_short | Machine Learning-based Water Quality Forecasting for Shenzhen Bay |
| title_sort | machine learning based water quality forecasting for shenzhen bay |
| topic | water quality forecasting machine learning Shenzhen Bay |
| url | http://www.renminzhujiang.cn/thesisDetails#10.3969/j.issn.1001-9235.2024.07.002 |
| work_keys_str_mv | AT xiongjianzhi machinelearningbasedwaterqualityforecastingforshenzhenbay AT xiongrui machinelearningbasedwaterqualityforecastingforshenzhenbay AT luhaiyan machinelearningbasedwaterqualityforecastingforshenzhenbay AT zhengyi machinelearningbasedwaterqualityforecastingforshenzhenbay |