# Machine Learning Approaches for Outdoor Air Quality Modelling: A Systematic Review
## 3 Results and Discussion
### Detailed Description
#### 3.2.4 Category 4: Hybrid Models and Extreme/Deep Learning  
This set of papers represents one of the rare categories in which forecasting models (10) are significantly more frequent than estimation models (2). The first estimation study consists of building a reliable model to predict NO\({}_{2}\) and NO\({}_{\rm x}\) with a high spatiotemporal resolution and over an extended period of time [60]. This work is carried out in three steps. First, multiple non-linear (traffic, meteorology, etc. ), fixed (e.g., population density) and spatial (e.g., elevation) effects are incorporated to characterize spatiotemporal variability of NO\({}_{2}\) and NO\({}_{\rm x}\) concentration. Second, the Ensemble Learning algorithm is applied to reduce variance and uncertainty in the prediction based on step 1. Third, an optimization process is implemented by tackling possible incomplete time-varying covariates recording (traffic, meteorology, etc.) in order to get a continuous time series dataset. The prediction performance is quite high (r\({}^{2}\approx\) 0.85). Traffic density, population density, and meteorology (wind speed and temperature) account for 9-13%, 5-11%, and 7-8% of the variance, respectively. Nevertheless, the method is less accurate for the prediction of low pollution levels. A different estimation approach is proposed by [PERSON] and [PERSON] [61]. The authors use an Extreme Learning Machine (ELM) to tackle the low convergence rate and the local minimum that characterize the NN algorithms. Their ELM consists of only 2-layer NN. The first layer (hidden layer) is fixed and random (its weight does not need to be adjusted). The second layer only is trained. They estimate a broad spectrum of contaminants (NO\({}_{2}\), NO\({}_{\rm x}\), O\({}_{3}\), PM\({}_{2.5}\), and SO\({}_{2}\)) from meteorological and time parameters. It appears that the ELM is significantly better than NN and Multiple Linear Regression (MLR), because it provides a higher accuracy and a lower computational cost. An even more advanced study is carried by [PERSON] et al. [62]. The authors propose an efficient non-linear machine learning algorithm for air quality forecasting (up to 48 h), which is easily updatable in'real time' thanks to a linear solution applied to the new data. Five algorithms are compared: MLR, Multi-Layer Neural Network (MLNN), ELM, updated-MLR, and updated-ELM. This approach involves two steps and predict three types of contaminants (O\({}_{3}\), PM\({}_{2.5}\), and NO\({}_{2}\)). Step 1 consists of the initial training stage of the algorithm. The 2 nd step involves a sequential learning phase, in order to proceed with an online (daily) update of the MLR and ELM, and only seasonally (trimonthly) update of the MLNN. The results show that the updated-ELM tends to outperform the other models, in terms of R\({}^{2}\) and MAE. In addition, the linear updating applied to MLR and ELM is less costly than updating a MLNN. Nevertheless, all the models tend to underpredict extreme values (the non-linear models even more than the linear models). Another type of forecasting involves hybrid models. This is the case of the study performed by [PERSON] et al. [63], who predict short and long-term CO from a hybrid model composed with Partial Least Square (PLS), for data selection, and SVM, for modelling. Overall, SVM-PLS seems more performant than traditional SVM, because both the precision and computational cost of the former is better than the latter. However, the accuracy is lower for hourly than daily forecasting. [PERSON] et al. [64] propose a different hybrid model to forecast air contaminants (O\({}_{3}\), NO\({}_{2}\), and PM\({}_{10}\)), with a special focus on the critical prediction of pollution peaks. The hybrid model is composed with NN and clustering and it is compared to the Multilayer Perceptron. The proposed algorithm outperforms the traditional NN, especially in the prediction of PM\({}_{10}\) and O\({}_{3}\) peaks of pollution. Since performing a forecasting consists mainly of predicting a time series, [PERSON] et al.