# 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
[65] propose a hybrid model that implements a NN and an Autoregressive Integrated Moving Average (ARIMA). The concentration of PM\({}_{2.5}\) are predicted in Beijing (China) from meteorological parameters, chemical variables, and microblog data. Such an approach allows for a good forecast for a few hours ahead, but the bigger the time lag, the bigger the error. A time series analysis is also used by [PERSON] and [PERSON] [66] to predict the AQI (NO\({}_{2}\) + PM\({}_{10}\) + O\({}_{3}\) + PM\({}_{2.5}\)) 1 h ahead and from the extraction of conceptual features (randomness and fuzziness of time series data). A hybrid model based on a technique of Cloud Model Granulation (CMG) and SVR is implemented in two steps. First, CMG uses probability and fuzzy theory to extract from time series the concept features of: randomness and fuzziness of the data. And second, after extracting these features, an SVR is used for the prediction per se. The performance of CMG-SVR is slightly better than the individual application of the same techniques (CMG and SVR alone) or a NN. Nevertheless, this method is still only able to make a short-term prediction (about 1 h ahead). The fuzzy approach to forecast PM\({}_{10}\) hourly concentration 1 h ahead is also used by [PERSON] et al. [67]. To tackle the randomness and fuzziness of the data, the hybrid model applies, first, a chance weight value to the target variable in order to minimize an outlier point (if any) that can be used, afterward, as a support vector point during the training process. The result of this study is one of the rare examples that shows a model that outperforms ensemble learning algorithms (Boosting and Stacking). [PERSON] et al. [68] propose to optimize the AQI forecasting by applying a 'decomposition and ensemble' technique in three steps: (i) decomposition of the original AQI time series into a set of independent components (i.e., frequencies or IMFs), (ii) prediction of each component (by ELM), and (iii) aggregation of the forecast values of all components. The hybrid model takes every eight successive days as AQI data inputs of the ELM to forecast the ninth day. The originality of the study is to propose a decomposition of the AQI time series into two phases-- by Complementary Ensemble Empirical Mode Decomposition (CEEMD), for low and medium frequencies, and by Variation Mode Decomposition (VMD), for high frequencies. This 2-phase decomposition provides a superior forecasting accuracy than 1-phase decomposition implementations. Fuzzy logic, ELM, and heuristic are put together in the model developed by [PERSON] et al. [66]. This study forecasts the AQI (based on PM\({}_{10}\), PM\({}_{2.5}\), SO\({}_{2}\), NO\({}_{2}\), CO, and O\({}_{3}\)) of six cities in China. Fuzzy logic is used as a feature selection, because relevant predictive contaminants are not the same according to the city. ELM and heuristic optimization algorithms are applied for a deterministic prediction of the contaminant concentrations. This hybrid model gives a better prediction than other NN and time series (e.g., ARIMA) algorithms but it is slightly slower than the NNs. More recently, a bunch of studies have implemented a Deep Learning approach to improve the accuracy of air pollution forecasting. For instance, [PERSON] et al. [69] propose a Deep Recurrent Neural Network (DRNN) method to forecast daily Air Quality Classification (AQC). This technique is divided into two steps. First, data of six pollutants are pre-processed, in order to group the concentrations into four categories or Individual Air Quality Index--IAQI (from unhealthy to good). Second, an RNN based on Long Short-Term Memory (LSTM) is used to perform the forecasting. LSTM is an RNN, which includes a memory that permits to learn the input sequence with longer time steps (e.g., problems related to time series). Despite the high expectations, the predictive accuracy of the model is not significantly better than the performance of the other tested algorithms (SVM and Ensemble Learning).