Forecasting Fine Particulate Matter Concentrations by In-Depth Learning Model According to Random Forest and Bilateral Long- and Short-Term Memory Neural Networks

Jie Zhao, Linjiang Yuan, Kun Sun, Han Huang, Panbo Guan and Ce Jia
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Jie Zhao: School of Environmental and Municipal Engineering, Xi’an University of Architecture and Technology, No. 13 Yanta Road, Xi’an 710055, China
Linjiang Yuan: School of Environmental and Municipal Engineering, Xi’an University of Architecture and Technology, No. 13 Yanta Road, Xi’an 710055, China
Kun Sun: SDU Life Cycle Engineering, Department of Green Technology, University of Southern Denmark, 5230 Odense, Denmark
Han Huang: School of Economics and Management, China University of Mining and Technology, Xuzhou 221116, China
Panbo Guan: Department of Energy Conservation and Green Development, The 714 Research Institute of CSSC, Beijing 100101, China
Ce Jia: School of Environment & Natural Resources, Renmin University of China, Beijing 100872, China

Sustainability, 2022, vol. 14, issue 15, 1-18

Abstract: Accurate prediction of fine particulate matter concentration in the future is important for human health due to the necessity of an early warning system. Generally, deep learning methods, when widely used, perform better in forecasting the concentration of PM 2.5 . However, the source information is limited, and the dynamic process is uncertain. The method of predicting short-term (3 h) and long-term trends has not been achieved. In order to deal with the issue, the research employed a novel mixed forecasting model by coupling the random forest (RF) variable selection and bidirectional long- and short-term memory (BiLSTM) neural net in order to forecast concentrations of PM 2.5 /0~12 h. Consequently, the average absolute percentage error of 1, 6, and 12 h shows that the PM 2.5 concentration prediction is 3.73, 9.33, and 12.68 μg/m 3 for Beijing, 1.33, 3.38, and 4.60 μg/m 3 for Guangzhou, 1.37, 4.19, and 6.35 μg/m 3 for Xi’an, and 2.20, 7.75, and 10.07 μg/m 3 for Shenyang, respectively. Moreover, the results show that the suggested mixed model is an advanced method that can offer high accuracy of PM 2.5 concentrations from 1 to 12 h post.

Keywords: Chinese regions; variable selection; meteorological factors; BiLSTM; prediction (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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