# Machine Learning Approaches for Outdoor Air Quality Modelling: A Systematic Review
## 3 Results and Discussion
### Detailed Description
While for the predictive features, meteorological factors account for 65% of the predictive accuracy (especially humidity, temperature, and solar radiation), also showing a higher predictive performance when the weather conditions are stronger (i.e., autumn). In this study, anthropogenic emissions (NH\({}_{3}\), CO, Organic Carbon, and NOx) exhibit a lower importance than meteorology and lower accuracy is registered for the regions with a sparser density of monitoring stations. Therefore, the accuracy relies on the complexity of the network. [PERSON] et al. [36] aim to identify the most robust machine learning algorithms to preserve a fair accuracy when O\({}_{3}\) monitoring failures may occur, by using five different algorithms--Bagging vs. Random Committee (RC) vs. Random Forest vs. Decision Tree vs. k-Nearest Neighbors (kNN). First, the prediction accuracy of the five machine learning algorithms is compared, then a hierarchical clustering technique is applied to identify how many models are needed to predict the O\({}_{3}\) in the region of Murcia, Spain. Two years of pollutant covariates (O\({}_{3}\), NO, NO\({}_{2}\), SO\({}_{2}\), NOx, PM\({}_{10}\), C\({}_{6}\)H\({}_{6}\), C\({}_{7}\)H\({}_{8}\), and XIL) and meteorological parameters (temperature, relative humidity, pressure, solar radiation, wind speed and direction) are used. The model is validated by the comparison of the coefficient of determination (predicted value/actual value) between the five models. Random Forest slightly outperforms the other models (r\({}^{2}\) = 0.85), followed by RC (r\({}^{2}\) = 0.83), Bagging and DT (r\({}^{2}\) = 0.82) and kNN (r\({}^{2}\) = 0.78). The best predictors are NOx, temperature, wind direction, wind speed, relative humidity, SO\({}_{2}\), NO, and PM\({}_{10}\). Finally, the hierarchical cluster shows that two models are enough to describe the studied regions. [PERSON] et al. [37] aim to identify the conditions under which high pollution emerges and to use a better generalized model. Hybrid system based on the combination of unsupervised clustering, Artificial Neural Networks (ANN) and Random Forest (RF) ensembles and fuzzy logic is used to predict multiple criteria pollutants in Athens, Greece. Twelve years of hourly data of CO, NO, NO\({}_{2}\), SO\({}_{2}\), temperature, relative humidity, pressure, solar radiation, wind speed and direction are used. Unsupervised clustering of the initial dataset is executed in order to re-sample the data vectors; while ensemble ANN modeling is performed using a combination of machine learning algorithms. The optimization of the modeling performance is done with Mamdani rule-based fuzzy inference system (FIS, used to evaluate the performance of each model) that exploits relations between the parameters affecting air quality. Specifically, self-organizing maps are used to perform dataset re-sampling, then ensembles of feedforward artificial neural networks and random forests are trained to clustered data vectors. The estimation model performance is quite good for CO (r\({}^{2}\) = 0.95, RF\({}_{-}\)FIS), NO (r\({}^{2}\) = 0.95, ensemble NN\({}_{-}\)FIS), NO and O\({}_{3}\) (r\({}^{2}\) = 0.91, RF) and SO\({}_{2}\) (r\({}^{2}\) = 0.78, ensemble regression). [PERSON] et al. [38] also employ a number of models (Multiple Linear Regression vs. Quantile Regression Model (QRM) vs. Generalized Additive Model vs. Boosted Regression Trees) to perform a comparative study on the performance for capturing the variability of PM\({}_{10}\). At the contrary of the linear regression, which considers variables distribution as a whole, the QRM defines the contribution (coefficient) of the predictors for different percentiles of PM\({}_{10}\) (here 10 percentiles or quantiles are used) to estimate the weight of each feature. Meteorological factors (wind speed, wind direction, temperature, humidity), chemical species (CO, NOx, SO\({}_{2}\)), and PM\({}_{10}\) of the previous day data for one year from Makkah (Saudi Arabia) are used.