Abstract
Dissolved oxygen content prediction plays an important role in the intelligence management of aquaculture systems. Compared with traditional point prediction, interval prediction can quantify the uncertainties effectively and more closes to the fact. Unfortunately, there is rarely study about it. In this paper, a novel interval prediction method based on a deep auto-regression recurrent neural network (DeepAR) is proposed to construct prediction intervals (PIs) directly. Besides, a variational mode decomposition (VMD) has been used to extract the frequency feature of the original data. Moreover, a multi-objective weighted optimization framework based on the sparrow swarm algorithm (SSA) was proposed to improve PIs accuracy. Finally, simulations with water quality datasets were conducted to show the effectiveness of the proposed model. The results demonstrated that the proposed model significantly improved PI quality and performance compared to the state-of-the-art method.
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The datasets used or analysed during the current study are available from the corresponding author on reasonable request.
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The code during the current study are available from the corresponding author on reasonable request.
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Funding
Finally, this work was supported in part by the National Natural Science Foundation of China under Grant 61871475, 61471-131, 61571444, in part by the special project of laboratory construction of Guangzhou Innovation Platform Construction Plan under Grant 201905010006, the Guangzhou Innovation Platform Construction Plan under Grant 2017B0101260016, the Foundation for High-level Talents in Higher Education of Guangdong Province under Grant 2017GCZX00014, 2016KZDXM0013, 2017KTSCX094, 2018LM2168, and the Bei**g Natural Science Foundation under Grant 4182023.
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Huang, J., Huang, Y., Hassan, S.G. et al. Dissolved oxygen content interval prediction based on auto regression recurrent neural network. J Ambient Intell Human Comput 14, 7255–7264 (2023). https://doi.org/10.1007/s12652-021-03579-x
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DOI: https://doi.org/10.1007/s12652-021-03579-x