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Li W, Zhao Y, Zhu Y, Dong Z, Wang F, Huang F. Research progress in water quality prediction based on deep learning technology: a review. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2024; 31:26415-26431. [PMID: 38538994 DOI: 10.1007/s11356-024-33058-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Accepted: 03/20/2024] [Indexed: 05/04/2024]
Abstract
Water, an invaluable and non-renewable resource, plays an indispensable role in human survival and societal development. Accurate forecasting of water quality involves early identification of future pollutant concentrations and water quality indices, enabling evidence-based decision-making and targeted environmental interventions. The emergence of advanced computational technologies, particularly deep learning, has garnered considerable interest among researchers for applications in water quality prediction because of its robust data analytics capabilities. This article comprehensively reviews the deployment of deep learning methodologies in water quality forecasting, encompassing single-model and mixed-model approaches. Additionally, we delineate optimization strategies, data fusion techniques, and other factors influencing the efficacy of deep learning-based water quality prediction models, because understanding and mastering these factors are crucial for accurate water quality prediction. Although challenges such as data scarcity, long-term prediction accuracy, and limited deployments of large-scale models persist, future research aims to address these limitations by refining prediction algorithms, leveraging high-dimensional datasets, evaluating model performance, and broadening large-scale model application. These efforts contribute to precise water resource management and environmental conservation.
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Affiliation(s)
- Wenhao Li
- School of Electrical and Automation Engineering, Nanjing Normal University, Nanjing, China
- Jiangsu Province Engineering Research Center of Environmental Risk Prevention and Emergency Response Technology, School of Environment, Nanjing, 210023, China
| | - Yin Zhao
- School of Electrical and Automation Engineering, Nanjing Normal University, Nanjing, China
| | - Yining Zhu
- Jiangsu Province Engineering Research Center of Environmental Risk Prevention and Emergency Response Technology, School of Environment, Nanjing, 210023, China
- Key Laboratory for Soft Chemistry and Functional Materials of Ministry of Education, Nanjing University of Science and Technology, Nanjing, 210094, Jiangsu, China
| | - Zhongtian Dong
- Key Laboratory for Soft Chemistry and Functional Materials of Ministry of Education, Nanjing University of Science and Technology, Nanjing, 210094, Jiangsu, China
| | - Fenghe Wang
- Jiangsu Province Engineering Research Center of Environmental Risk Prevention and Emergency Response Technology, School of Environment, Nanjing, 210023, China
- Key Laboratory for Soft Chemistry and Functional Materials of Ministry of Education, Nanjing University of Science and Technology, Nanjing, 210094, Jiangsu, China
| | - Fengliang Huang
- School of Electrical and Automation Engineering, Nanjing Normal University, Nanjing, China.
- Jiangsu Province Engineering Research Center of Environmental Risk Prevention and Emergency Response Technology, School of Environment, Nanjing, 210023, China.
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Zhao T, Shen Z, Zhong P, Zou H, Han M. Detection and prediction of pathogenic microorganisms in aquaculture (Zhejiang Province, China). ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2024; 31:8210-8222. [PMID: 38175512 DOI: 10.1007/s11356-023-31612-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/10/2023] [Accepted: 12/14/2023] [Indexed: 01/05/2024]
Abstract
The detection and prediction of pathogenic microorganisms play a crucial role in the sustainable development of the aquaculture industry. Currently, researchers mainly focus on the prediction of water quality parameters such as dissolved oxygen for early warning. To provide early warning directly from the pathogenic source, this study proposes an innovative approach for the detection and prediction of pathogenic microorganisms based on yellow croaker aquaculture. Specifically, a method based on quantitative polymerase chain reaction (qPCR) is designed to detect the Cryptocaryon irritans (Cri) pathogenic microorganisms. Furthermore, we design a predictive combination model for small samples and high noise data to achieve early warning. After performing wavelet analysis to denoise the data, two data augmentation strategies are used to expand the dataset and then combined with the BP neural network (BPNN) to build the fusion prediction model. To ensure the stability of the detection method, we conduct repeatability and sensitivity tests on the designed qPCR detection technique. To verify the validity of the model, we compare the combined BPNN to long short-term memory (LSTM). The experimental results show that the qPCR method provides accurate quantitative measurement of Cri pathogenic microorganisms, and the combined model achieves a good level. The prediction model demonstrates higher accuracy in predicting Cri pathogenic microorganisms compared to the LSTM method, with evaluation indicators including mean absolute error (MAE), recall rate, and accuracy rate. Especially, the accuracy of early warning is increased by 54.02%.
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Affiliation(s)
- Tong Zhao
- College of Information and Electrical Engineering, China Agricultural University, 17 Tsinghua East Road, Beijing, 100083, China
- National Innovation Center for Digital Fishery, China Agricultural University, Beijing, 100083, China
| | - Zhencai Shen
- College of Science, China Agricultural University, Beijing, 100083, China
- National Innovation Center for Digital Fishery, China Agricultural University, Beijing, 100083, China
- Key Laboratory of Smart Farming Technologies for Aquatic Animals and Livestock, Ministry of Agriculture and Rural Affairs, Beijing, 100083, China
- Beijing Engineering and Technology Research Center for Internet of Things in Agriculture, Beijing, 100083, China
| | - Ping Zhong
- College of Information and Electrical Engineering, China Agricultural University, 17 Tsinghua East Road, Beijing, 100083, China
- College of Science, China Agricultural University, Beijing, 100083, China
- National Innovation Center for Digital Fishery, China Agricultural University, Beijing, 100083, China
- Key Laboratory of Smart Farming Technologies for Aquatic Animals and Livestock, Ministry of Agriculture and Rural Affairs, Beijing, 100083, China
- Beijing Engineering and Technology Research Center for Internet of Things in Agriculture, Beijing, 100083, China
| | - Hui Zou
- College of Science, China Agricultural University, Beijing, 100083, China.
- National Innovation Center for Digital Fishery, China Agricultural University, Beijing, 100083, China.
- Key Laboratory of Smart Farming Technologies for Aquatic Animals and Livestock, Ministry of Agriculture and Rural Affairs, Beijing, 100083, China.
- Beijing Engineering and Technology Research Center for Internet of Things in Agriculture, Beijing, 100083, China.
| | - Mingming Han
- Zhejiang Academy of Agricultural Sciences, Zhejiang, 310021, China
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