A review of the application of machine learning in water quality evaluation
With the rapid increase in the volume of data on the aquatic environment, machine learning has become an important tool for data analysis, classification, and prediction. Unlike traditional models used in water-related research, data-driven models based on machine learning can efficiently solve more...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2022-06-01
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| Series: | Eco-Environment & Health |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772985022000163 |
| _version_ | 1797347047934787584 |
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| author | Mengyuan Zhu Jiawei Wang Xiao Yang Yu Zhang Linyu Zhang Hongqiang Ren Bing Wu Lin Ye |
| author_facet | Mengyuan Zhu Jiawei Wang Xiao Yang Yu Zhang Linyu Zhang Hongqiang Ren Bing Wu Lin Ye |
| author_sort | Mengyuan Zhu |
| collection | DOAJ |
| description | With the rapid increase in the volume of data on the aquatic environment, machine learning has become an important tool for data analysis, classification, and prediction. Unlike traditional models used in water-related research, data-driven models based on machine learning can efficiently solve more complex nonlinear problems. In water environment research, models and conclusions derived from machine learning have been applied to the construction, monitoring, simulation, evaluation, and optimization of various water treatment and management systems. Additionally, machine learning can provide solutions for water pollution control, water quality improvement, and watershed ecosystem security management. In this review, we describe the cases in which machine learning algorithms have been applied to evaluate the water quality in different water environments, such as surface water, groundwater, drinking water, sewage, and seawater. Furthermore, we propose possible future applications of machine learning approaches to water environments. |
| first_indexed | 2024-03-08T11:41:53Z |
| format | Article |
| id | doaj.art-e6d896283c1c4a6bb4115f6bd965e4bc |
| institution | Directory Open Access Journal |
| issn | 2772-9850 |
| language | English |
| last_indexed | 2024-03-08T11:41:53Z |
| publishDate | 2022-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Eco-Environment & Health |
| spelling | doaj.art-e6d896283c1c4a6bb4115f6bd965e4bc2024-01-25T05:24:26ZengElsevierEco-Environment & Health2772-98502022-06-0112107116A review of the application of machine learning in water quality evaluationMengyuan Zhu0Jiawei Wang1Xiao Yang2Yu Zhang3Linyu Zhang4Hongqiang Ren5Bing Wu6Lin Ye7State Key Laboratory of Pollution Control and Resource Reuse, School of Environment, Nanjing University, Nanjing 210023, ChinaState Key Laboratory of Pollution Control and Resource Reuse, School of Environment, Nanjing University, Nanjing 210023, ChinaState Key Laboratory of Pollution Control and Resource Reuse, School of Environment, Nanjing University, Nanjing 210023, ChinaState Key Laboratory of Pollution Control and Resource Reuse, School of Environment, Nanjing University, Nanjing 210023, ChinaState Key Laboratory of Pollution Control and Resource Reuse, School of Environment, Nanjing University, Nanjing 210023, ChinaState Key Laboratory of Pollution Control and Resource Reuse, School of Environment, Nanjing University, Nanjing 210023, ChinaCorresponding authors.; State Key Laboratory of Pollution Control and Resource Reuse, School of Environment, Nanjing University, Nanjing 210023, ChinaCorresponding authors.; State Key Laboratory of Pollution Control and Resource Reuse, School of Environment, Nanjing University, Nanjing 210023, ChinaWith the rapid increase in the volume of data on the aquatic environment, machine learning has become an important tool for data analysis, classification, and prediction. Unlike traditional models used in water-related research, data-driven models based on machine learning can efficiently solve more complex nonlinear problems. In water environment research, models and conclusions derived from machine learning have been applied to the construction, monitoring, simulation, evaluation, and optimization of various water treatment and management systems. Additionally, machine learning can provide solutions for water pollution control, water quality improvement, and watershed ecosystem security management. In this review, we describe the cases in which machine learning algorithms have been applied to evaluate the water quality in different water environments, such as surface water, groundwater, drinking water, sewage, and seawater. Furthermore, we propose possible future applications of machine learning approaches to water environments.http://www.sciencedirect.com/science/article/pii/S2772985022000163Machine learningWater qualityEvaluationPrediction |
| spellingShingle | Mengyuan Zhu Jiawei Wang Xiao Yang Yu Zhang Linyu Zhang Hongqiang Ren Bing Wu Lin Ye A review of the application of machine learning in water quality evaluation Eco-Environment & Health Machine learning Water quality Evaluation Prediction |
| title | A review of the application of machine learning in water quality evaluation |
| title_full | A review of the application of machine learning in water quality evaluation |
| title_fullStr | A review of the application of machine learning in water quality evaluation |
| title_full_unstemmed | A review of the application of machine learning in water quality evaluation |
| title_short | A review of the application of machine learning in water quality evaluation |
| title_sort | review of the application of machine learning in water quality evaluation |
| topic | Machine learning Water quality Evaluation Prediction |
| url | http://www.sciencedirect.com/science/article/pii/S2772985022000163 |
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