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Article

Predicting Aquaculture Water Quality Using Machine Learning Approaches

1
School of Resources and Environmental Engineering, Ludong University, Yantai 264025, China
2
CAS Key Laboratory of Coastal Environmental Processes and Ecological Remediation, Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences, Yantai 264003, China
3
Shandong Key Laboratory of Coastal Environmental Processes, Yantai 264003, China
*
Author to whom correspondence should be addressed.
Academic Editor: Jeff Strock
Water 2022, 14(18), 2836; https://doi.org/10.3390/w14182836
Received: 27 July 2022 / Revised: 5 September 2022 / Accepted: 8 September 2022 / Published: 12 September 2022
(This article belongs to the Special Issue Advances in Hydrogeology and Groundwater Management Research)
Good water quality is important for normal production processes in industrial aquaculture. However, in situ or real-time monitoring is generally not available for many aquacultural systems due to relatively high monitoring costs. Therefore, it is necessary to predict water quality parameters in industrial aquaculture systems to obtain useful information for managing production activities. This study used back propagation neural network (BPNN), radial basis function neural network (RBFNN), support vector machine (SVM), and least squares support vector machine (LSSVM) to simulate and predict water quality parameters including dissolved oxygen (DO), pH, ammonium-nitrogen (NH3-N), nitrate nitrogen (NO3-N), and nitrite-nitrogen (NO2-N). Published data were used to compare the prediction accuracy of different methods. The correlation coefficients of BPNN, RBFNN, SVM, and LSSVM for predicting DO were 0.60, 0.99, 0.99, and 0.99, respectively. The correlation coefficients of BPNN, RBFNN, SVM, and LSSVM for predicting pH were 0.56, 0.84, 0.99, and 0.57. The correlation coefficients of BPNN, RBFNN, SVM, and LSSVM for predicting NH3-N were 0.28, 0.88, 0.99, and 0.25, respectively. The correlation coefficients of BPNN, RBFNN, SVM, and LSSVM for predicting NO3-N were 0.96, 0.87, 0.99, and 0.87, respectively. The correlation coefficients of BPNN, RBFNN, SVM, and LSSVM predicted NO2-N with correlation coefficients of 0.87, 0.08, 0.99, and 0.75, respectively. SVM obtained the most accurate and stable prediction results, and SVM was used for predicting the water quality parameters of industrial aquaculture systems with groundwater as the source water. The results showed that the SVM achieved the best prediction effect with accuracy of 99% for both published data and measured data from a typical industrial aquaculture system. The SVM model is recommended for simulating and predicting the water quality in industrial aquaculture systems. View Full-Text
Keywords: industrial aquaculture; machine learning; support vector machine; water quality prediction industrial aquaculture; machine learning; support vector machine; water quality prediction
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MDPI and ACS Style

Li, T.; Lu, J.; Wu, J.; Zhang, Z.; Chen, L. Predicting Aquaculture Water Quality Using Machine Learning Approaches. Water 2022, 14, 2836. https://doi.org/10.3390/w14182836

AMA Style

Li T, Lu J, Wu J, Zhang Z, Chen L. Predicting Aquaculture Water Quality Using Machine Learning Approaches. Water. 2022; 14(18):2836. https://doi.org/10.3390/w14182836

Chicago/Turabian Style

Li, Tingting, Jian Lu, Jun Wu, Zhenhua Zhang, and Liwei Chen. 2022. "Predicting Aquaculture Water Quality Using Machine Learning Approaches" Water 14, no. 18: 2836. https://doi.org/10.3390/w14182836

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