A novel hybrid algorithm with static and dynamic models for air quality index forecasting

Two data-driven algorithms, back propagation neural network (BPNN) and support vector regression (SVR), are adopted to predict air quality index (AQI) in Jiangsu Province. Meanwhile, the static model, the dynamic model of daily training and the half-daily training are established to validate the performance of algorithms comprehensively. The fundamental advantage of support vector is that less data in the full set is selected to efficiently capture the whole characteristics, whereas BPNN is more accurate in description of the high dimensional models since the parameters are well trained. The comparisons between two algorithms for the above models demonstrate that BPNN outperforms SVR in terms of accuracy since most of the mean absolute percentage errors of BPNN are less than 10%, which decrease 3% compared with that of SVR, whereas the computational cost of SVR is much less than that of BPNN. Furthermore, a novel hybrid model, the SVR-BPNN model, is proposed to further predict and analyze the AQI, which performs as fairly well as BPNN but is less time-consuming.