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Predicting Aquaculture Water Quality Using Machine Learning Approaches

文献类型：期刊论文

作者Li, Tingting3; Lu, Jian1,2; Wu, Jun3; Zhang, Zhenhua3; Chen, Liwei3
刊名WATER
出版日期2022-09-01
卷号14期号:18页码:15
关键词industrial aquaculture machine learning support vector machine water quality prediction
DOI10.3390/w14182836
通讯作者Wu, Jun(wujunlisa@163.com)
英文摘要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.
WOS关键词NEURAL-NETWORKS ; UNCERTAINTY
WOS研究方向Environmental Sciences & Ecology ; Water Resources
语种英语
WOS记录号WOS:000856883900001
资助机构Youth Innovation Team Project for Talent Introduction and Cultivation in Universities of Shandong Province ; Taishan Scholars Program of Shandong Province ; Science and Technology Service Network Initiative of the Chinese Academy of Sciences ; Two-Hundred Talents Plan of Yantai
源URL[http://ir.yic.ac.cn/handle/133337/31700]  
专题烟台海岸带研究所_中科院海岸带环境过程与生态修复重点实验室
烟台海岸带研究所_近岸生态与环境实验室
通讯作者Wu, Jun
作者单位1.Shandong Key Lab Coastal Environm Proc, Yantai 264003, Peoples R China
2.Chinese Acad Sci, Yantai Inst Coastal Zone Res, CAS Key Lab Coastal Environm Proc & Ecol Remediat, Yantai 264003, Peoples R China
3.Ludong Univ, Sch Resources & Environm Engn, Yantai 264025, Peoples R China
推荐引用方式
GB/T 7714		 Li, Tingting,Lu, Jian,Wu, Jun,et al. Predicting Aquaculture Water Quality Using Machine Learning Approaches[J]. WATER,2022,14(18):15.
APA Li, Tingting,Lu, Jian,Wu, Jun,Zhang, Zhenhua,&Chen, Liwei.(2022).Predicting Aquaculture Water Quality Using Machine Learning Approaches.WATER,14(18),15.
MLA Li, Tingting,et al."Predicting Aquaculture Water Quality Using Machine Learning Approaches".WATER 14.18(2022):15.

入库方式： OAI收割

来源：烟台海岸带研究所

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