## 3 Results
### Exhaustive Descriptions of Included Studies
#### Group 2: Regression
Compared to RF and bootstrap aggregating techniques, the proposed model demonstrated better results. _A spatio-temporal prediction model based on support vector machine regression: Ambient Black Carbon in three New England States [42]_: [PERSON] et al. studied the prediction of black carbon applying nu-Support Vector Regression (nu-SVR). The dataset covered a 12 year period (2000-2011) of the greater Boston area, Cape Cod, Western and Central Massachusetts which were captured from different sources, such as National Institute of Environmental Health Sciences (NIEHS), the Northeast States for Coordinated Air Use Management (NESCAUM)[43], The Interagency Monitoring of Protected Visual Environments (\"IMPROVE\") [44], The U.S. Environmental Protection Agency (EPA), and the Normative Aging Study (NAS). Apart from air quality and meteorological data, the following variables also were included in the study: proximity to transportation, topographical characteristics, neighbourhood characteristics. R\({}^{2}\) was applied as an evaluation metric. The results showed that the proposed method could provide an efficient prediction. _Particulate Matter Air Pollutants Forecasting using Inductive Learning Approach [45]_: [PERSON] et al. applied an inductive learning approach to forecast PM\({}_{10}\) for the next three days using the data of the previous 8 days. The two methods that were used in this study are M5P and REPTree. The data set included air quality (sulfur dioxide, nitrogen monoxide, carbon monoxide, nitrogen oxides, nitrogen dioxide, particulate matter, ozone, o-xylene, m-xylene, benzene, toluene, p-xylene, butadiene, ethyl-benzene), and meteorological data (temperature, relative humidity, solar radiation, atmospheric pressure, wind direction, wind speed, precipitations), over a period of January 2009 to December 2009, January 2011 to December 2011, and January 2015 to April 2015. With the help of PCA, the most correlated variables were selected, including SO\({}_{2}\), NO\({}_{2}\), air temperature and relative humidity. The evaluation metrics used in this study were R, MAE and RMSE. The results showed that M5P enhances the accuracy of the prediction. _Wind-sensitive Interpolation of Urban Air Pollution Forecasts [46]_: is focused on the prediction NO, NO\({}_{2}\), SO\({}_{2}\), O\({}_{3}\) and interpolation real-time forecasts in city Valencia. Wind aspect was used for prediction taking into account the factor in Valencia. This air quality data were taken from the Valencia City Council, and the meteorological data (temperature, relative humidity, pressure, wind speed, rain) were taken from the Meteorological Agency of the Government of Spain (AEMET) [47]. Additional to these data traffic intensity features were extracted (traffic level in the surrounding stations and traffic level 3 h before). The following machine learning techniques were applied, including Linear Regression (LR), Quantile Regression (QR) with lasso method, KNN with k = 10, DTR, and RF. To measure the methods mentioned above, the authors used RMSE. The results showed that RF had comparable better results. Afterwards, the authors analysed the wind direction effect on air pollution to enrich the forecasting model, and they applied Local IDW for interpolation purposes, which includes a wind direction factor. _Comparing the Performance of Statistical Models for Predicting PM\({}_{10}\) Concentrations [48]_: is focused on the hourly PM\({}_{10}\) prediction. The following machine learning methods were applied, including MLR, QR, Generalised Additive Model (GAM), and Boosted Regression Trees 1-way (BRT1) and 2-way (BRT2). The dataset included air quality (CO, SO\({}_{2}\), NO, NO\({}_{2}\), PM\({}_{10}\)) and meteorological (wind speed, wind direction, temperature, relative humidity, rainfall, pressure) data of the city of Makkah during 2012. To evaluate the methods, the Mean Bias Error (MBE), MAE, RMSE, the fraction of prediction within a Factor of Two (FACT2), R, and IA were applied. The results showed that QR outperformed other methods.