Air-Quality Prediction Based on the EMD–IPSO–LSTM Combination Model

Yuan Huang, Junhao Yu, Xiaohong Dai, Zheng Huang and Yuanyuan Li
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Yuan Huang: School of Information and Electrical Engineering, Hebei University of Engineering, Handan 056038, China
Junhao Yu: School of Information and Electrical Engineering, Hebei University of Engineering, Handan 056038, China
Xiaohong Dai: School of Information and Electrical Engineering, Hebei University of Engineering, Handan 056038, China
Zheng Huang: School of Information and Electrical Engineering, Hebei University of Engineering, Handan 056038, China
Yuanyuan Li: School of Information and Electrical Engineering, Hebei University of Engineering, Handan 056038, China

Sustainability, 2022, vol. 14, issue 9, 1-18

Abstract: Owing to climate change, industrial pollution, and population gathering, the air quality status in many places in China is not optimal. The continuous deterioration of air-quality conditions has considerably affected the economic development and health of China’s people. However, the diversity and complexity of the factors which affect air pollution render air quality monitoring data complex and nonlinear. To improve the accuracy of prediction of the air quality index (AQI) and obtain more accurate AQI data with respect to their nonlinear and nonsmooth characteristics, this study introduces an air quality prediction model based on the empirical mode decomposition (EMD) of LSTM and uses improved particle swarm optimization (IPSO) to identify the optimal LSTM parameters. First, the model performed the EMD decomposition of air quality data and obtained uncoupled intrinsic mode function (IMF) components after removing noisy data. Second, we built an EMD–IPSO–LSTM air quality prediction model for each IMF component and extracted prediction values. Third, the results of validation analyses of the algorithm showed that compared with LSTM and EMD–LSTM, the improved model had higher prediction accuracy and improved the model fitting effect, which provided theoretical and technical support for the prediction and management of air pollution.

Keywords: long short-term memory; EMD; improved PSO; air-quality prediction (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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