## 3 Results
### Exhaustive Descriptions of Included Studies
#### 3.2.3 Group 3: Ensemble
The data used in this paper included the observed hourly values of O\({}_{3}\) and NO\({}_{\times}\) concentrations, surface temperature, relative humidity, wind speed, direction, dew point temperature, surface pressure, and precipitation. For evaluation purposes, IA was applied. The results showed that yearly IA was in the range of 0.84-0.89 and yearly Rs were in the range of 0.72-0.80. As a limitation was mentioned that high-ozone episodes were underpredicted, particularly during the high-ozone season (April-September). _A Deep Spatial-Temporal Ensemble Model for Air Quality Prediction_[PERSON] and [PERSON] proposed a deep Spatial-Temporal Ensemble (STE) model, which included weather pattern-based partitioning strategy, spatial correlation and temporal predictor based on deep LSTM. The dataset consisted of air quality data (CO, NO\({}_{2}\), SO\({}_{2}\), O\({}_{3}\), PM\({}_{10}\), PM\({}_{2.5}\)) and weather forecast data (temperature, humidity, wind speed, wind direction) from May 2013 to April 2017 from 35 monitoring stations in Beijing, China. To evaluate the effectiveness of the model, MAE, RMSE and accuracy were used. The following baselines were used: LR, Regression Tree (RT), DNN, FFA (2018). The results showed that STE outperformed other methods. _Early Air Pollution Forecasting as a Service: an Ensemble Learning Approach_[PERSON] and [PERSON] (2018) : is focused on the air pollution prediction using Multi-channel Ensemble Learning via Supervised Assignment (MELSA) algorithm reported in web service. The case study for this research was Beijing city. The air pollution data consisted of PM\({}_{2.5}\), PM\({}_{10}\), SO\({}_{2}\), CO, NO\({}_{\times}\), O\({}_{3}\), and meteorological data were relative humidity, dew point temperature, surface pressure. The aim of this study is using the features mentioned above as an input to predict air quality index (AQI) for 24-72 h temporal resolution. The proposed method was compared to the following methods, including stacking, RF, AdaBoosting, bagging, WRFChem (2018), CMAQ (2018), and neural network. As evaluation metrics were used Relative Absolute Error (RAE), Relative Squared Error (RSE) and R. The results showed that the proposed method outperformed other methods. A Comprehensive Evaluation of Air Pollution Prediction Improvement by a Machine Learning Method [59]_: [PERSON] et al. applied the machine learning techniques in order to predict air pollution with better accuracy. As an input variables were taken air quality (PM\({}_{2.5}\), PM\({}_{10}\), SO\({}_{2}\), NO\({}_{2}\), CO, O\({}_{3}\)), meteorological (wind speed, direction, pressure, humidity, temperature), chemical components (organic carbon, black carbon, dust) from October 2013 to April 2015. The methods (RF, gradient boosting, SVM, DT and combined models of these models) were applied in 74 cities in China. The results showed that in the case of including more features, the accuracy would increase, also the combination of the methods performed better results than each method separately. Ensemble forecasting with machine learning algorithms for ozone, nitrogen dioxide and PM\({}_{10}\) on the Prev'Air platform [60]: describes the Prev'Air operational platform which is served to generate a daily map for forecasting O\({}_{3}\), NO\({}_{2}\) and PM\({}_{10}\). The data were collected between 2008 and 2010 years. To evaluate the performance indicators in order to include in the platform, Normalized Mean Square Error (NMSE), correlation, daily observed mean vs. daily simulated mean were applied. The Discounted Ridge Regression (DRR) was applied in order to compute new weights before the prediction; afterwards, the authors compared it to Best Model and to the Best Constant Linear Combination. RMSE metric was used to evaluate the methods. The result showed that respectively O\({}_{3}\) was reduced by about 29%, 35% and 19% for hourly, daily and peak, NO\({}_{2}\) was reduced by about 19%, 26% and 20% for hourly, daily and peak, PM\({}_{10}\) was reduced by about 17%, 19% and 11% for hourly, daily and peak.