Atmospheric PM2.5 Prediction Model Based on Principal Component Analysis and SSA–SVM

He Gong, Jie Guo, Ye Mu, Ying Guo, Tianli Hu, Shijun Li (), Tianye Luo and Yu Sun ()
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He Gong: College of Information Technology, Jilin Agricultural University, Changchun 130118, China
Jie Guo: College of Information Technology, Jilin Agricultural University, Changchun 130118, China
Ye Mu: College of Information Technology, Jilin Agricultural University, Changchun 130118, China
Ying Guo: College of Information Technology, Jilin Agricultural University, Changchun 130118, China
Tianli Hu: College of Information Technology, Jilin Agricultural University, Changchun 130118, China
Shijun Li: College of Information Technology, Wuzhou University, Wuzhou 543003, China
Tianye Luo: College of Information Technology, Jilin Agricultural University, Changchun 130118, China
Yu Sun: College of Information Technology, Jilin Agricultural University, Changchun 130118, China

Sustainability, 2024, vol. 16, issue 2, 1-16

Abstract: This paper uses an enhanced sparrow search algorithm (SSA) to optimise the support vector machine (SVM) by considering the emission of air pollution sources as the independent variable. Consequently, it establishes a PM2.5 concentration prediction model to improve the prediction accuracy of fine particulate matter PM2.5 concentration. First, the principal component analysis is applied to extract key variables affecting air quality from high-dimensional air data to train the model while removing unnecessary redundant variables. Adaptive dynamic weight factors are introduced to balance the global and local search capabilities and accelerate the convergence of the SSA. Second, the SSA–SVM prediction model is defined using the optimised SSA to continuously update the network parameters and achieve the rapid prediction of atmospheric PM2.5 concentration. The findings demonstrate that the optimised SSA–SVM prediction method can quickly predict atmospheric PM2.5 concentration, using the cyclic search method for the best solution to update the model, proving the method’s effectiveness. Compared with other methods, this approach has a small prediction error, a high prediction accuracy and better practical value.

Keywords: PM2.5 concentration; principal component analysis; support vector machine (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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