Title: Medium and long-term trend prediction of urban air quality based on deep learning

Authors: Zhencheng Wang; Feng Xie

Addresses: School of Computer, Electronics and Information, Guangxi University, Nanning 530004, China; Guangxi Key Laboratory of Multimedia Communications and Network Technology (Cultivating Base), Guangxi University, Nanning 530004, China ' Guangxi Vocational College of Safety Engineering, Nanning 530100, China

Abstract: In order to overcome the low accuracy of traditional air quality change prediction methods, this paper designs a medium and long-term prediction method of urban air quality change trend based on deep learning. The deep learning network is constructed, and the air quality prediction process is designed by using the deep learning algorithm to optimise the air quality prediction model. The deep belief network is initialised by unsupervised training, and the data is supervised by back propagation algorithm. By continuously optimising the network weights to avoid the network falling into local optimum, the medium and long-term accurate prediction of air quality change trend can be realised. The experimental results show the AQI index value of the prediction results of the model has a high fitting degree with the actual value, and the evaluation values of RMSE, MAE, MSE and SMAPE are 2.608%, 2.613%, 2.07% and 0.9513 respectively, which proves the effectiveness of the method.

Keywords: deep learning; air quality; meteorological characteristics; forecast effect; air quality index; AQI.

DOI: 10.1504/IJETM.2022.120724

International Journal of Environmental Technology and Management, 2022 Vol.25 No.1/2, pp.22 - 37

Received: 30 Dec 2020
Accepted: 04 Mar 2021
Published online: 04 Feb 2022 *

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