Evolving Spiking Neural Network Model for PM2.5 Hourly Concentration Prediction Based on Seasonal Differences: A Case Study on Data from Beijing and Shanghai

Hengyuan Liu; Guibin Lu; Yangjun Wang; Nikola Kasabov

doi:10.4209/aaqr.2020.05.0247

Evolving Spiking Neural Network Model for PM2.5 Hourly Concentration Prediction Based on Seasonal Differences: A Case Study on Data from Beijing and Shanghai

Hengyuan Liu¹, Guibin Lu This email address is being protected from spambots. You need JavaScript enabled to view it.¹, Yangjun Wang², Nikola Kasabov^3,4

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¹School of Economics, Shanghai University, Shanghai 200444, China
²School of Environmental and Chemical Engineering, Shanghai University, Shanghai 200444, China
³School of Engineering, Computing and Mathematical Sciences, Auckland University of Technology, Auckland, New Zealand
⁴Intelligent Systems Research Centre, Ulster University, Londonderry, UK

Received: May 19, 2020
Revised: August 11, 2020
Accepted: August 25, 2020

Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

Download Citation: ||https://doi.org/10.4209/aaqr.2020.05.0247

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Cite this article:

Liu, h., Lu, G., Wang, Y., Kasabov, N. (2021). Evolving Spiking Neural Network Model for PM_2.5 Hourly Concentration Prediction Based on Seasonal Differences: A Case Study on Data from Beijing and Shanghai. Aerosol Air Qual. Res. 21, 200247. https://doi.org/10.4209/aaqr.2020.05.0247

HIGHLIGHTS

A Staging-eSNN model is proposed to predict PM_2.5 hourly concentration.
Seasonal difference in diurnal variation of PM_2.5 have been considered and evaluated.
The available data are processed to capture informative patterns by the Staging-eSNN.

ABSTRACT

In recent years, the dangers that air pollutants pose to human health and the environment have received widespread attention. Although accurately predicting the air quality is essential to managing pollution and developing control policies, traditional forecasting models have not been able to simulate the seasonal and diurnal variation in air pollutant concentrations. Furthermore, inadequate processing of the available spatio-temporal data has precluded the capture of predictive historical patterns. Therefore, we have developed a staging evolving spiking neural network (eSNN) model named Staging-eSNN that first employs a time series clustering algorithm to distinguish the seasonal from the diurnal variation in the PM_2.5 concentration. We then predict the concentrations in Beijing and Shanghai 1, 3, 6, 12 and 24 hours in advance. Various evaluation indicators show that the Staging-eSNN model achieves higher performance than the support vector regression (SVR), random forest (RF) and other eSNN models.

Keywords: Air pollutant prediction, PM2.5 hourly concentration, Seasonality, Evolving spiking neural networks, Time series clustering

Aerosol Air Qual. Res. 21 :200247 -. https://doi.org/10.4209/aaqr.2020.05.0247