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Deep reservoir calculation model and its application in the field of temperature and humidity prediction

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Abstract

In the context of global climate change, an effective prediction of temperature and humidity can improve people’s living environment and quality of life. To handle the problem of temperature and humidity series prediction, this study proposes a deep reservoir calculation model, namely DeepSALR. The DeepSALR uses a deep neural network to pre-train the datapoints before capturing multiple degrees of abstract information in the series. Then, the feature extraction datapoints are fed into the reservoir calculation model for supervised prediction. Considering the problem that using single neurons as the excitation function is prone to produce singular solutions, this study proposes to use wavelet neurons to replace part of sigmoid neurons, and then obtains an enhanced DeepSALR model, namely eDeepSALR. At the same time, this study also proposes a hybrid particle swarm optimization (HPSO) algorithm to determine the node numbers in deep neural networks. Extensive experimental results show that the eDeepSALR is capable of solving temperature and humidity prediction problems, and that it outperforms existing models in terms of prediction accuracy and short-term memory.

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Acknowledgements

This work was supported by the Special Foundation for Beijing Tianjin Hebei Basic Research Cooperation (No. J210008, H2021202008), the Inner Mongolia Discipline Inspection and Supervision Big Data Laboratory (No. IMDBD202105) and Hebei Province doctoral student innovation ability training funding project (No. CXZZBS2022040).

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Authors and Affiliations

  1. School of Electronics and Information Engineering, Hebei University of Technology, 5340 Xiping Road, Beichen District, Tianjin, 300401, People’s Republic of China

    Minghui Zhang, Yatong Zhou & Yabo Liu

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  1. Minghui Zhang
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  2. Yatong Zhou
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  3. Yabo Liu
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Correspondence to Yatong Zhou.

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Zhang, M., Zhou, Y. & Liu, Y. Deep reservoir calculation model and its application in the field of temperature and humidity prediction. Appl Intell 53, 4393–4414 (2023). https://doi.org/10.1007/s10489-022-03685-z

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