# Machine Learning Approaches for Outdoor Air Quality Modelling: A Systematic Review
## 3 Results and Discussion
### Detailed Description
Step 2 generates simulated satellite ultraviolet spectral absorption (UV spectra) of representative ozone profiles from each cluster. Step 3 consists of improving the classification effectiveness by reducing the input data through a Principal Component Analysis. In step 4 the classification model is applied for assigning an ozone profile class corresponding to a given UV spectrum. And in step 5 the ozone profile shape is scaled according to the total ozone columns. The algorithm is tested by comparing the predicted value to the observed value. 11 clusters are obtained, and the estimation error is lower than 10%. This technique can be considered an encouraging approach to predict ozone shapes based on a classification framework rather than the conventional inversion methods, even if its computational cost is relatively high. A slightly different approach is proposed by [PERSON] et al. [53]. This work aims to build global (1 km \(\times\) 1 km) and local (100 m \(\times\) 100 m) models to predict PM\({}_{2.5}\) from AOD and PM\({}_{2.5}\)/PM\({}_{10}\) ratio. It is based on an SVM algorithm to predict the concentration of PM\({}_{2.5}\) in Switzerland. The dataset is composed of 11 years of observations and a broad spectrum of features, such as: Planetary boundary layers, meteorological factors, sources of pollution, AOD, elevation, and land use. The results show that the method is able to predict PM\({}_{2.5}\) by using data provided by sparse monitoring stations. Another technique consists of supplementing sparse accurate station monitoring by lower fidelity dense mobile sensors, with the aim of getting a fine spatial granularity [54]. Seven Regression models are used to predict CO concentrations in Sidney, Australia. The study is divided into three stages. First, the models are built from seven years of historical data of static monitoring (15 stations) and three years of mobile monitoring. Second, the performances of each algorithm are compared. And third, field trials are conducted to validate the models. The results show that the best predictions are provided by Support Vector Regression (SVR), Decision Tree Regression (DTR), and Random Forest Regression (RFR). The validations on the field suggest that SVR has the highest spatial resolution estimation and indicates boundaries of polluted area better than the other regression models. In addition, a web application is implemented to collect data from static and mobile sensors, and to inform users. Finally, a quite different study uses image processing to predict pollution from a video-camera analysis [55]. Again, several families of machine learning algorithms are tested and compared. The method involves four sequential steps, which are: (i) camera records, (ii) image processing, (iii) feature extraction, and (iv) machine learning classification. The objective is to assess the severity of the pollution cloud emitted by factories from 12 features characterizing the image, such as-- luminosity, surface of the cloud, color, and duration of the cloud. Although all the algorithms provide a similar performance, Decision Tree is the only one to be systematically classified among the models with the best (i) robustness of the parameter setting (= easy to configure), (ii) robustness to the size of the learning set, and (iii) computational cost. This study introduces interesting performance metrics based on 'efficiency' (higher weight for the correct classification of 'critical events' than 'noncritical events') instead of 'accuracy' (same weight for any event). In addition, with the objective to tackle the issue of unbalanced classes and since the authors are more interested in critical events (black cloud), which are less represented, some 'noncritical events' are removed from the learning set in order to get a balanced representation of the classes. Nevertheless, the application of this method is limited to the assessment of the contamination produced by plants.