Research of dissolved oxygen prediction in recirculating aquaculture systems based on deep belief network |
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| Institution: | 1. National Innovation Center for Digital Fishery, China Agricultural University, Beijing 100083, China;2. Beijing Engineering and Technology Research Center for Internet of Things in Agriculture, China Agricultural University, Beijing 100083, China;3. Precision Agricultural Technology Integration Research Base (Fishery), Ministry of Agriculture and Rural Affairs, Beijing 100083, China;1. School of Information Science & Engineering, Changzhou University, Changzhou, China;2. Jiangsu Key Construction Laboratory of IoT Application Technology, Taihu University of Wuxi, Wuxi, China;3. Changzhou Technical Institute of Tourism & Commerce, Changzhou, China;1. School of Information Science & Engineering, Changzhou University, Changzhou 213164, China;2. Changzhou Technical Institute of Tourism & Commerce, Changzhou 213023, China;1. College of Information, Guangdong Ocean University, Zhanjiang, Guangdong 524025, China;2. College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China;3. Beijing ERC for Internet of Things in Agriculture, China Agricultural University, Beijing 100083, China;4. Beijing ERC for Advanced Sensor Technology in Agriculture, China Agricultural University, Beijing 100083, China;1. Young Researchers and Elite Club, Hamedan Branch, Islamic Azad University, Hamedan, Iran;2. Department of Water Engineering, College of Agriculture, Bu-Ali Sina University, Hamedan, Iran;3. Istanbul Technical University, Civil Engineering Department, Hydraulics Division, 34469 Maslak, Istanbul, Turkey |
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| Abstract: | Recirculating aquaculture has received more and more attention because of its high efficiency of treatment and recycling of aquaculture wastewater. The content of dissolved oxygen is an important indicator of control in recirculating aquaculture, its content and dynamic changes have great impact on the healthy growth of fish. However, changes of dissolved oxygen content are affected by many factors, and there is an obvious time lag between control regulation and effects of dissolved oxygen. To ensure the aquaculture production safety, it is necessary to predict the dissolved oxygen content in advance. The prediction model based on deep belief network has been proposed in this paper to realize the dissolved oxygen content prediction. A variational mode decomposition (VMD) data processing method has been adopted to evaluate the original data space, it takes the data which has been decomposed by the VMD as the input of deep belief network (DBN) to realize the prediction. The VMD method can effectively separate and denoise the raw data, highlight the relations among data features, and effectively improve the quality of the neural network input. The proposed model can quickly and accurately predict the dissolved oxygen content in time series, and the prediction performance meets the needs of actual production. When compared with bagging, AdaBoost, decision tree and convolutional neural network, the VMD-DBN model produces higher prediction accuracy and stability. |
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| Keywords: | Dissolved oxygen prediction Recirculating aquaculture system Deep learning Deep belief network Variational mode decomposition |
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