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      A hybrid intelligent method for three-dimensional short-term prediction of dissolved oxygen content in aquaculture

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          Abstract

          A precise predictive model is important for obtaining a clear understanding of the changes in dissolved oxygen content in crab ponds. Highly accurate interval forecasting of dissolved oxygen content is fundamental to reduce risk, and three-dimensional prediction can provide more accurate results and overall guidance. In this study, a hybrid three-dimensional (3D) dissolved oxygen content prediction model based on a radial basis function (RBF) neural network, K-means and subtractive clustering was developed and named the subtractive clustering (SC)-K-means-RBF model. In this modeling process, K-means and subtractive clustering methods were employed to enhance the hyperparameters required in the RBF neural network model. The comparison of the predicted results of different traditional models validated the effectiveness and accuracy of the proposed hybrid SC-K-means-RBF model for three-dimensional prediction of dissolved oxygen content. Consequently, the proposed model can effectively display the three-dimensional distribution of dissolved oxygen content and serve as a guide for feeding and future studies.

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          Most cited references 36

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          A tutorial guide to geostatistics: Computing and modelling variograms and kriging

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            An efficient self-organizing RBF neural network for water quality prediction.

            This paper presents a flexible structure Radial Basis Function (RBF) neural network (FS-RBFNN) and its application to water quality prediction. The FS-RBFNN can vary its structure dynamically in order to maintain the prediction accuracy. The hidden neurons in the RBF neural network can be added or removed online based on the neuron activity and mutual information (MI), to achieve the appropriate network complexity and maintain overall computational efficiency. The convergence of the algorithm is analyzed in both the dynamic process phase and the phase following the modification of the structure. The proposed FS-RBFNN has been tested and compared to other algorithms by applying it to the problem of identifying a nonlinear dynamic system. Experimental results show that the FS-RBFNN can be used to design an RBF structure which has fewer hidden neurons; the training time is also much faster. The algorithm is applied for predicting water quality in the wastewater treatment process. The results demonstrate its effectiveness.
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              Prediction of dissolved oxygen content in river crab culture based on least squares support vector regression optimized by improved particle swarm optimization

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                Author and article information

                Contributors
                Role: Writing – review & editing
                Role: Writing – original draft
                Role: Data curation
                Role: Data curation
                Role: Project administration
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                21 February 2018
                2018
                : 13
                : 2
                Affiliations
                [1 ] College of Information and Electrical Engineering, China Agricultural University, Beijing, China
                [2 ] Key Laboratory of Agricultural Information Acquisition Technology, Ministry of Agriculture, Beijing, P.R. China
                [3 ] Beijing Engineering and Technology Research Center for Internet of Things in Agriculture, Beijing, P.R. China
                Tokai University, JAPAN
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Article
                PONE-D-17-32698
                10.1371/journal.pone.0192456
                5821340
                29466394
                © 2018 Chen et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                Counts
                Figures: 9, Tables: 4, Pages: 17
                Product
                Funding
                Funded by: Innovative model & demonstration based water management for resource efficiency in integrated multitrophic aquaculture and horticulture systems
                Award ID: 619137
                Award Recipient :
                Funded by: Research and Demonstration of Intelligent Regulation Technology Equipments for Large - scale Freshwater Fish Health Breeding
                Award ID: Z171100001517016
                Award Recipient :
                This paper was supported by the EU cooperation project—“Innovative model & demonstration based water management for resource efficiency in integrated multitrophic aquaculture and horticulture systems”, No. 619137 and Beijing Science and Technology Plan projects “Research and Demonstration of Intelligent Regulation Technology Equipments for Large - scale Freshwater Fish Health Breeding”, No.Z171100001517016.
                Categories
                Research Article
                Earth Sciences
                Marine and Aquatic Sciences
                Water Quality
                Dissolved Oxygen
                Physical Sciences
                Mathematics
                Numerical Analysis
                Interpolation
                Biology and Life Sciences
                Agriculture
                Aquaculture
                Earth Sciences
                Marine and Aquatic Sciences
                Bodies of Water
                Ponds
                Computer and Information Sciences
                Neural Networks
                Biology and Life Sciences
                Neuroscience
                Neural Networks
                Research and Analysis Methods
                Mathematical and Statistical Techniques
                Statistical Methods
                Forecasting
                Physical Sciences
                Mathematics
                Statistics (Mathematics)
                Statistical Methods
                Forecasting
                Earth Sciences
                Marine and Aquatic Sciences
                Water Quality
                Engineering and Technology
                Equipment
                Measurement Equipment
                Custom metadata
                All relevant data are within the paper and its Supporting Information files.

                Uncategorized

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