## 3 Results
### Exhaustive Descriptions of Included Studies
#### Group 2: Regression  
_Comparative Analysis of Machine Learning Techniques for Predicting Air Quality in Smart Cities_[39]: [PERSON] et al. used different models for predicting air quality, such as DTR, Random Forest Regression (RFR), MLP and GBR. The dataset used in this study included year, month, day, hour, season, PM\({}_{2.5}\), dew point, temperature, humidity, pressure, combined wind direction, accumulated wind speed, hourly precipitation, accumulated precipitation. For evaluation criteria, MAE and RMSE were used. Here were the best methods of each city in terms of RMSE and MAE: Beijing city-DTR (RMSE-0.07) and RFR (MAE-16.92%); Shanghai city-MLP (RMSE-0.03, MAE-13.84%); Shenyang city-RFR (RMSE -0.059) and MLP (MAE-13.65%); Guangzhou city-MLP (RMSE-0.045, MAE-12.2%); Chengdu city-RFR (RMSE-0.08, MAE-10.5%). In terms of processing time, DTR and RFR were faster compared to the other two methods. After hyperparameter tuning on a single Spark node, the results showed that RF was the best technique, which also was able to find the peak values. For future work, the authors mentioned using additional factors related to air pollution. _Air-Pollution Prediction in Smart Cities through Machine Learning Methods: A Case of Study in Murcia, Spain_[40]: was focused on the prediction of the ozone level (O\({}_{3}\)) in the region of Murcia (Spain). The machine learning techniques used in this paper are: bagging-with REPTree classifier, a random committee with random tree based classifier, RF, M5P and an instance-based technique with K Nearest Neighbors (KNN). The dataset included average per hour of chemical elements (NO, NO\({}_{2}\), SO\({}_{2}\), NO\({}_{\omega}\), PM\({}_{10}\), C\({}_{6}\)H\({}_{6}\), toluene (C\({}_{7}\)H\({}_{8}\)),ylene (XIL)) and climatic parameters (temperature, relative humidity, wind direction, wind speed, atmospheric pressure and solar radiation) for each day for 2013-2014 years. For the evaluation intention, the models were measured by MAE, RMSE and R\({}^{2}\). The results showed that RF had lower RMSE and MAE than the other machine learning models. Related to R\({}^{2}\), above 0.75R\({}^{2}\) was considered a satisfactory result and all the methods obtained higher from this threshold. The results indicated R\({}^{2}\) setting between 80% and 90% overall. In addition, except for this, [PERSON] et al. applied the Wilcoxon Signed Ratings Test, which confirmed that RF had better results than the other machine learning techniques with 99% confidence level. After choosing the best model, the next step was to do hierarchical clustering in order to know how many models would be needed for O\({}_{3}\) prediction in the region of Murcia. For that purpose, the Discrete Wavelet Transform (DWT) and Euclidean distance measurement were applied. The output indicated that air pollution monitoring area can be divided into two zones: three cities except Caravaca were unified as one cluster and Caravaca remained as a separate cluster. For future work, new elements (PM\({}_{10}\), SO\({}_{2}\)) must be considered and analysed, and, also another improvement would be to transfer information to the target groups. _Air Pollution Forecasting Model Based on Chance Theory and Intelligent Techniques [41]_: to forecast PM\({}_{10}\) hourly concentration for the next hour, [PERSON] et al. suggested to apply chance Weighted Support Vector Regression (chWSVR). The method can deal with interval-valued uncertainty. The dataset consisted of air pollution data (SO\({}_{2}\), CO, O\({}_{3}\), PM\({}_{10}\), PM\({}_{2.5}\), NO\({}_{x}\)) and meteorological data (air temperature, relative humidity, atmospheric pressure, planetary boundary layer height, wind speed and direction) collected from 2007 to 2010. With the data mentioned above, temporal variables were also included as an input. The following parameters were used as an evaluation metrics: RMSE, R, fisher r-to-z transformation (z') and t-value (significant at \(\alpha\) 0.05).