## 3 Results
### Exhaustive Descriptions of Included Studies
#### 3.2.3 Group 3: Ensemble  
_A predictive data feature exploration-based air quality prediction approach [52]_: [PERSON] et al. proposed Light Gradient Boosting Machine (LightGBM) model and combining predictive and historical data executed prediction of the PM\({}_{2.5}\) concentration over the next 24 h. This method helped to process the high-dimensional large-scale data and support parallel learning. The problem of the lack of data was solved by applying the sliding window mechanism, which increases the training dimensions to millions. PCA dimension reduction method was used to discard redundant information. Afterwards, all data were integrated, including air quality (PM\({}_{2.5}\), PM\({}_{10}\), NO\({}_{2}\), CO, O\({}_{3}\), SO\({}_{2}\) of the 35 air quality monitoring stations in Beijing from 2017 to 2018), temporal, meteorological (temperature, weather, humidity, wind direction, wind speed), weather forecast and statistical features. The proposed method was compared to Adaboost, GBDT, XGboost, DNN and also with LGBT without forecasting. To evaluate the prediction model, three evaluation functions were used, including, SMAPE, Mean Square Error (MSE), and MAE. The results showed that LightGBM outperformed other methods. This is due to that LightGBM is a histogram-based algorithm that supports parallel learning, which causesfaster training rate and higher accuracy. In addition, it is worth noting that the proposed method outperformed LightGBM without predictive data. _A multiple kernel learning approach for air quality prediction_[PERSON] et al. (2019): [PERSON] et al. proposed multiple kernel learning model with support vector classifier (MKSVC) as the base learner, which combines feature selection, metric learning and ensemble method for predicting air quality. For learning kernels, the centred alignment approach was applied, and for determining the optimal number of kernels, a boosting approach was applied. The case study was Hong Kong and Beijing. Air pollutant dataset contained Fine Suspended Particulates, NO\({}_{2}\), NO\({}_{\times}\), O\({}_{3}\), RSP, and SO\({}_{2}\). The meteorology dataset contained temperature, atmospheric pressure at weather station level, atmospheric pressure reduced to mean sea level, pressure tendency, relative humidity, mean wind direction, mean wind speed, dew, dew point. Timestamp features were contained month, week, day and hour. The prediction targets for this study were the Air Quality Health Index (AQHI) in Hong Kong and the PM\({}_{2.5}\) Individual Air Quality Level (IAQL) in Beijing. The model was compared to ARIMA, RF and SVM, MLP and LSTM. For evaluating the effectiveness of the methods, the authors used accuracy, MSE, Weighted Precision (WP), Weighted Recall (WR), and Weighted F1-score (WF). The results of forecasting future 1, 3, 6, 9, and 12 hours' AQHI in Hong Kong showed that MKSVC was the best among all methods. MKSVC was best also for forecasting the PM\({}_{2.5}\) IAQL of Beijing. Compared to other methods, the proposed approach demonstrated relatively good performances for long-term prediction and severe air pollution prediction; however, for effective air quality prediction, more exploration should be done. _A data ensemble approach for real-time air quality forecasting using extremely randomised trees and deep neural networks_[PERSON] et al. (2018): [PERSON] et al. applied extremely randomised tree (extra-trees method) and DNN, generalised ensemble models, for forecasting ozone concentration. The ensemble model integrated two regression models: low- and high-ozone peak models. Two models were generalised, such as merging all samples from all sources and uniformly distributing the samples based on target ozone peaks. In addition, regularised models were developed in order to focus more on episodes with high-ozone peaks more significant than the threshold (90 Parts Per Billion (PPB)).