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Prediction Model and Influencing Factors of CO 2 Micro/Nanobubble Release Based on ARIMA-BPNN

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  • Bingbing Wang

    (College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China
    Key Laboratory of Agricultural Information Standardization, Ministry of Agriculture and Rural Affairs, Beijing 100083, China)

  • Xiangjie Lu

    (College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China
    Key Laboratory of Agricultural Information Standardization, Ministry of Agriculture and Rural Affairs, Beijing 100083, China)

  • Yanzhao Ren

    (School of Computer Science and Engineering, Beijing Technology and Business University, Beijing 100048, China)

  • Sha Tao

    (College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China
    Key Laboratory of Agricultural Information Standardization, Ministry of Agriculture and Rural Affairs, Beijing 100083, China)

  • Wanlin Gao

    (College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China
    Key Laboratory of Agricultural Information Standardization, Ministry of Agriculture and Rural Affairs, Beijing 100083, China)

Abstract

The quantitative prediction of CO 2 concentration in the growth environment of crops is a key technology for CO 2 enrichment applications. The characteristics of micro/nanobubbles in water make CO 2 micro/nanobubble water potentially useful for enriching CO 2 during growth of crops. However, few studies have been conducted on the release characteristics and factors influencing CO 2 micro/nanobubbles. In this paper, the factors influencing CO 2 release and changes in CO 2 concentration in the environment are discussed. An autoregressive integrated moving average and backpropagation neural network (ARIMA-BPNN) model that maps the nonlinear relationship between the CO 2 concentration and various influencing factors within a time series is proposed to predict the released CO 2 concentration in the environment. Experimental results show that the mean absolute error and root-mean-square error of the combination prediction model in the test datasets were 9.31 and 17.48, respectively. The R 2 value between the predicted and measured values was 0.86. Additionally, the mean influence value (MIV) algorithm was used to evaluate the influence weights of each input influencing factor on the CO 2 micro/nanobubble release concentration, which were in the order of ambient temperature > spray pressure > spray amount > ambient humidity. This study provides a new research approach for the quantitative application of CO 2 micro/nanobubble water in agriculture.

Suggested Citation

  • Bingbing Wang & Xiangjie Lu & Yanzhao Ren & Sha Tao & Wanlin Gao, 2022. "Prediction Model and Influencing Factors of CO 2 Micro/Nanobubble Release Based on ARIMA-BPNN," Agriculture, MDPI, vol. 12(4), pages 1-18, March.
    • Handle: RePEc:gam:jagris:v:12:y:2022:i:4:p:445-:d:777695
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    References listed on IDEAS

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    3. Zou, Ping & Yang, Jingsong & Fu, Jianrong & Liu, Guangming & Li, Dongshun, 2010. "Artificial neural network and time series models for predicting soil salt and water content," Agricultural Water Management, Elsevier, vol. 97(12), pages 2009-2019, November.
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