# Machine Learning Approaches for Outdoor Air Quality Modelling: A Systematic Review
## 3 Results and Discussion
### Detailed Description
Three years of NO, NO2, CO, SO2, O3, and PM10 data are used in Aviles, Spain. The model is 10-fold cross validated with the observed data, resulting in best performance using PUK for NOx and O3 (r2 = 0.8). [PERSON] et al. [23] also employ SVM to get the most reliable predictive model of air quality (AQI) by considering monitoring data of three cities in China (Beijing, Tianjin, and Shijiazhuang). Two years of last-day AQI values, pollutant concentrations (PM2.5, PM10, SO2, CO, NO2, and O3), meteorological factors (temperature, wind direction and velocity), and weather description (ex. cloudy/sunny, or rainy/snowy, etc.) are used as predicting features. The dataset is split into a training set and testing set through a 4-fold cross validation technique. To validate the model performance, the results are compared to the observed data. The model performance is especially improved when the surrounding cities' air quality information is included. [PERSON] et al. [45] also use SVM to forecast air quality (NO\({}_{2}\), SO\({}_{2}\), O\({}_{3}\), SPM) from pollutants and meteorological data in Macau (China). The training is performed on three years of data and tested on one whole year and then just on January and July for seasonal predictions. The Pearson correlation is used to identify the best predictors for each pollutant and different kernels are used to test which of the predictors or models get the best results. The Pearson correlation is also employed to determine how many days of data are optimal for forecasting. The model performance is validated by comparison to the observed data resulting in best kernel--linear and RBF, in polynomial (summer), confirming that SVM performance depends on the appropriate choice of the kernel. In the forecasting model category, there are two articles that use NN, and meteorology and pollutants as predicting features. [PERSON] et al. [46] aim to build a model that forecasts AQI one day ahead by using an Ensemble Neural Network that processes selected factors using PEK-based machine learning for 16 main cities in China (three years of data). First a selection of the best predictors (PM\({}_{2.5}\), PM\({}_{10}\), and SO\({}_{2}\)) is performed, based on Partial Mutual Information (PMI), which measures the degree of predictability of the output variable knowing the input variable, and then the daily AQI value is predicted, through PEK-based machine learning, by using the previous day's meteorological and pollution conditions. The model is validated by a comparison between actual and predicted value, resulting in an average value of r\({}^{2}\) = 0.58. Meanwhile, [PERSON] and [PERSON] [47] present the spatiotemporal modelling of O\({}_{3}\) in Athens (Greece) also using NN, specifically developing 741 ANN (21 years of data). A multi-layer feed forward and back propagation optimization are employed. The best results are conceived with three approaches--the simplest ANN, the best ANN for each case of predictions, and the dynamic variation of NN. The performance of the model is validated with the observed data and between NN approaches, resulting in r\({}^{2}\) = 0.799, performing the best for the surrounding stations. This approach is concluded as a good option for modeling with monitoring station data due to very common gaps. Finally, [PERSON] et al. [48] aim to extract rules for guiding the forecasting of particulate matter (PM\({}_{10}\)) concentration levels using Reduced Error Pruning Tree (REPTree) vs. M5P (an inductive learning algorithm). The principal component analysis is used to select the best predictors, and then 27 months of eight previous-days of PM\({}_{10}\), NO\({}_{2}\), SO\({}_{2}\), temperature and relative humidity data are used to predict PM\({}_{10}\) in Romania. The validation is performed by comparing the results of two models with the observed values, concluding that M5P provides the more accurate prediction of the short-term PM\({}_{10}\) concentrations (r\({}^{2}\) = 0.81, for one day ahead and r\({}^{2}\) = 0.79 for two days ahead).