Table 21.
The short summary of hybrid system for air pollution.
| List of Recent Research on the Application of HS in the Field of Air Pollution | |
|---|---|
| Author | Main Contribution |
| Chen et al. [17] | Combining numerical forecast (WRF) with statistical analysis (temporal synoptic index) to forecast high-PM10 concentration in Beijing. This hybrid forecast system forecasts high-PM pollution events is more accurately than current forecast methods. It combines the strengths of various methods while avoiding the disadvantages found when statistical forecast methods are used alone. |
| Zhou et al. [99] | Established a hybrid EEMD-GRNN model to forecast the concentration of pollutants in Xi’an, which was shown to be superior to other conventional models. |
| Qin et al. [97] | Proposed the CS-EEMD-BPANN model for forecasting PM concentrations in Beijing, Shanghai, Guangzhou and Lanzhou. The forecasting result is improved and this method is more stable than BPNN and EEMD-BPANN. |
| Qin et al. [100] | Using an a priori algorithm mined the spatial and temporal associations of intercity PM, also mined cross spatial and temporal associations of PM10 and PM2.5 in the Jing-Jin-Ji region (China). |
| Wang et al. [68] | They used HANN, HSVM and Taylor expansion forecasting model in Taiyuan. The innovation involved in this approach is that it sufficiently and validly utilizes the useful residual information on an incomplete input variable condition. |
| Feng et al. [101] | 1. Using trajectory based geographic parameter as an extra input to ANN model; 2. Applying forecast strategy at different scales and then sum them up; 3. The backward trajectories from Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model were used to track the transport corridors of air masses. |
| Xu et al. [52] | Proposed ICEEMD-SVM-WOA model and FE model. This model not only forecast the concentrate on air pollutants, but also evaluates the effectiveness of the new forecast system by fuzzy evaluation method. |
| Wongsathan et al. [102] | Proposed a fundamental hybrid forecast model. This model can improve the performance of the forecast models, the exogenous variable may be considered as well as the modified of the hybrid algorithm |