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 PM2.5 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.
Bibliographical noteFunding Information:
The work was financially supported by the National Natural Science Foundation of China (No. 41105102) and the National Key R&D Program of China (No. 2018YFC0213600).
© The Author(s).
- Air pollutant prediction, PM2.5 hourly concentration, Seasonality, Evolving spiking neural networks
- Time series clustering