## 3 Results
### Exhaustive Descriptions of Included Studies
#### 3.2.1 Group 1: Neural Network (NN)  
_Prediction of Air Pollution Concentration Based on mRMR and Echo State Network [12]_: to predict PM\({}_{2.5}\), [PERSON] and [PERSON] employed a Supplementary Leaky Integrator Echo State Network (SLI-ESN) which can memorise historical information. First of all, they used minimum Redundancy Maximum Relevance (mRMR) feature selection method to solve a problem related to data redundancy, which increased computational speed. Then they applied phase space reconstruction to extract evolutionary information of relevant variables, and finally, to perform prediction, SLI-ESN was applied. The following methods were used for comparison purposes: Echo State Network (ESN), Leaky Integrator Echo State Network (LI-ESN), Extreme Learning Machine (ELM), Hierarchical ELM and Stacked Auto-Encoder. The dataset consisted of air pollution (PM\({}_{2.5}\), particulate matter with diameter equal to 10 micrometers (PM\({}_{10}\)), nitrogen dioxide (NO\({}_{2}\)), carbon monoxide (CO), ground-level ozone (O\({}_{3}\)), sulfur dioxide (SO\({}_{2}\))) and meteorological (temperature, pressure, humidity, wind speed, wind direction) data. The predictive indicators used for evaluating the model were Root Mean Square  
Figure 4: Used machine learning algorithms. Figure 3: Case study of the publications. Error (RMSE), Normalised Root Mean Square Error (NRMSE), Mean Absolute Error (MAE), Symmetric Mean Absolute Percentage Error (SMAPE) and Pearson correlation coefficient (R). The results showed that compared to other methods, SLI-ESN performed better results. In addition, the authors compared methods, in terms of the time factor, and the results showed that ESN and ELM based methods were faster than deep learning model, because the latter one consumes much time in training step for optimal subset selection and model optimization. The proposed method was not the fastest one, but it was in an acceptable time frame. About the limitations, the main problem was that for the longer term, the result was not satisfactory. _Spatiotemporal Prediction of PM2. Five Concentrations at Different Time Granularities Using IDW-BLSTM [13]_: [PERSON] et al. applied the combination of Bi-directional Long Short-Term Memory (BLSTM) network and the Inverse Distance Weighting (IDW) technique for the spatiotemporal prediction of PM\({}_{2.5}\) concentration at different time granularities (hourly, daily, and weekly granularities). The proposed method was compared to AutoRegressive Integrated Moving Average (ARIMA), ElasticNet, Support Vector Regression (SVR), Gradient Boosting Decision Tree (GBDT), Artificial Neural Network (ANN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), BLSTM, Convolutional Neural Network-LSTM (CNN-LSTM). The authors used different indicators, including RMSE, MAE and Mean Absolute Percentage Error (MAPE) to evaluate the methods. The results showed that IDW-BLSTM, CNN-LSTM, BLSTM, LSTM, and RNN had better performances compared to other methods. Overall, the IDW helped BLSTM to improve accuracy by 5.6%, and the final results of the proposed methods were RMSE-8.24, MAE-4.80, MAPE(%)-9.01. This study included analysis related to finding optimal window size for different temporal granularities. The result showed that when the window size was five, it was the optimal size for the hourly as well as for daily and weekly granularities. The limitation was that only the historic air pollution data were used and other relevant data (the meteorological and urban information) were not included. _Air Pollution Forecasting Using a Deep Learning Model Based on 1D Convnets and Bidirectional GRU [14]_: [PERSON] et al. introduced short-term forecasting method for PM\({}_{2.5}\) which included the Convolutional-based Bidirectional Gated Recurrent Unit (CBGRU) combined with 1D convnets and Bidirectional Gated Recurrent Unit (BGRU) neural networks. The former one was responsible for feature extraction, and the latter one was for time series forecasting.