Table 23.
Different models of air pollution forecast.
| Method Types | Authors | Models | Main Conclusions |
|---|---|---|---|
| Statistical methods | Silibello et al. [108] | Kalman filter (KF) and Hybrid forecast (HF) | Use two adjustment techniques, the HF and the KF, to improve the accuracy of forecasting supplied by an air quality forecast system |
| Huebnerova et al. [109] | Generalized linear models with log–link and gamma distribution | It’s shown that the predicted meteorological variables are used to predict well though comparative analysis of the two models | |
| Artificial intelligence methods | Catalano et al. [110] | ANN and ARIMAX | Forecasted the extreme concentrations by integrating the two models into an ensemble |
| Feng et al. [111] | SVM-GABPNN | Proposed a hybrid model which SVM was used to classify data, GA used to optimize the BPNN model. | |
| Bai et al. [24] | W-BPNN | Using wavelet transform to realize feature extraction and characterization of air pollutants | |
| Siwek et al. [112] | Wavelet transformation, the multilayer perceptron, radial basis function, Elman network, SVM and linear ARX model | Decomposed the data into the wavelet coefficients and used different NN to individual prediction, then combined the few predictors in the ensemble. This approach does not require very exhaustive information about air pollutants, and it has the ability of allowing the nonlinear relationships between very different predictor variables. | |
| Hybrid methods | Feng et al. [101] | Hybrid ANN | Used trajectory based geographic parameter as an extra input to ANN model; using wavelet transformation decomposed original series into a few sub-series with lower variability |
| Fu et al. [113] | RM-GM-FFNN | Enhanced FFNN model with RM and GM to assess the possible correlation between different input variables for improving forecast accuracy | |
| Song et al. [4] | ANF, Distribution functions, | Proposed interval prediction method and ANF to address the uncertainty of PMs according to the pollutant emission distribution. | |
| Three dimensional models | Luo et al. [27] | Models-3/CMAQ | Provided a method of analyzing the change of pollutants’ concentration in the condition of lacking practical pollution data. |
| Grell et al. [92] | Fully coupled online chemistry with the WRF model | The accuracy of forecasting of meteorological modules and chemical modules under different conditions of separation and coupling is explored. The result indicate that the ability to predict a slight increase | |
| Other methods | Kurt et al. [26] | Neural networks based on geographic forecasting models | The models which considered the geographic factor performed better than the models which unconsidered. |
| Pan et al. [103] | GM Grey relational analysis |
Selected 30 indexes of 5 categories, and find mainly impact factors by using grey relational analysis, then used GM (1, 1) model to forecast the concentration of pollutants |