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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

            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.

            Contributors
            ORCID: http://orcid.org/0000-0002-9635-8044, 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
            29466394 5821340 10.1371/journal.pone.0192456 PONE-D-17-32698
            © 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.

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