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

Bingbing Wang, Xiangjie Lu, Yanzhao Ren, Sha Tao and Wanlin Gao
Additional contact information
Bingbing Wang: College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China
Xiangjie Lu: College of Information and Electrical Engineering, China Agricultural University, 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
Wanlin Gao: College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China

Agriculture, 2022, vol. 12, issue 4, 1-18

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.

Keywords: CO 2 prediction; CO 2 enrichment; CO 2 micro/nanobubble; combined prediction model (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2077-0472/12/4/445/pdf (application/pdf)
https://www.mdpi.com/2077-0472/12/4/445/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jagris:v:12:y:2022:i:4:p:445-:d:777695

Access Statistics for this article

Agriculture is currently edited by Ms. Leda Xuan

More articles in Agriculture from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().