Prediction of Ozone Concentration in Semi-Arid Areas of China Using a Novel Hybrid Model

Abstract

The semi-arid regions of China support delicately balanced ecosystems that are likely to be significantly affected by small changes in the level of atmospheric ozone. To predict ozone concentrations in such areas, this paper proposes a new hybrid forecasting model called an ARMT-CPSO-BP neural network, which is based on the association rule mining technique (ARMT), a chaotic particle swarm optimization algorithm (CPSO) and a back-propagation (BP) neural network (CPSO-BP neural network). This paper first uses the ARMT to find correlations between meteorological variables and ozone concentrations. The significant correlation coefficients are then fed into the CPSO-BP neural network to obtain a prediction. Finally, the predicted results are compared with the results obtained from the BP neural network and the regression model. The comparison shows that the proposed hybrid model is superior to both the BP neural network and the regression model. The hybrid model reduces the square root mean square error (RMSE) by 31.6% compared to the BP model and by 23.8% compared to the regression model. The hybrid model is promising for forecasting ozone concentration in arid and semi-arid areas.

Keywords: ozone prediction, association rule mining technique, neural network, chaotic, particle swarm optimization