## 3 Results
### Exhaustive Descriptions of Included Studies
#### 3.2.1 Group 1: Neural Network (NN)
For checking the effectiveness of the method, the authors compared it to SVR, Gradient Boosting Regression (GBR), Decision Tree Regression (DTR), simple RNN, LSTM, Gated Recurrent Unit (GRU) and BGRU. The data used in this study were from the machine learning repository at the University of California, Irvine (UCI) [15] and meteorological data from Beijing Capital International Airport. RMSE, MAE and SMAPE were used for evaluation purposes. Comparing to the traditional ones, the prediction results demonstrated that the error of the CBGRU model was lower, including RMSE-14.5319, MAE-10.4789 and SMAPE-0.2055. _A Deep CNN-LSTM Model for Particulate Matter (PM\({}_{2.5}\)) Forecasting in Smart Cities [16]_: to forecast PM\({}_{2.5}\), the combination of Convolutional Neural Network (CNN) and LSTM was applied. CNN was responsible for features extraction, LSTM was for analysing the extracted features and for estimating the PM\({}_{2.5}\) concentration of the next point in time. The method proposed here (APNet) used PM\({}_{2.5}\) concentration, cumulated wind speeds, and cumulated hours of rain over the last 24 h in order to predict PM\({}_{2.5}\) for the next hour. Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT), Multilayer Perceptron (MLP), CNN, and LSTM were used for comparison purposes. As an evaluation metrics, the authors selected MAE, RMSE, R and Index of Agreement (IA). The results showed that, although CNN and LSTM separately achieved good results, the combination of them, the proposed CNN-LSTM model (APNet) was better having the following results: MAE-14.63446, RMSE-24.22874, R-0.959986, IA-0.97831. _A Sequence-to-Sequence Air Quality Predictor Based on the n-Step Recurrent Prediction [17]_: taking into account that sequence-to-sequence (seq2 seq) had some problems (slow training speed, error accumulation), [PERSON] et al. proposed to use an Attention-based Air Quality Predictor (AAQP) with n-step recurrent prediction. To accelerate the training process, RNN in encoder was replaced with a Fully Connected (FC) layer and also considering that FC layer was not as powerful as RNN during the process of sequential data, position embedding was applied. To improve the accuracy, n-step recurrent prediction was applied. MAE and determination coefficient (R\({}^{2}\)) were used as performance metrics. The following methods were used to compare and measure the proposed method: ANN, SVM, GRU, LSTM, seq2 seq, seq2 seq-mean, seq2 seq-attention and n-step AAQP. The Olympic Center station (smaller fluctuations of PM\({}_{2.5}\)) and Dongsi station (big fluctuations of PM\({}_{2.5}\)) were selected as the target stations. The results showed that attention-based models demonstrated better results and also recurrent prediction induced better results compared to direct prediction. The proposed AAQP (GRU) method in the Olympic Center station had similar performances to the original seq2 seq model with attention. According to the MAE score, the best performance was seq2 seq-attention (GRU)-33.109, and for R\({}^{2}\) was seq2 seq-attention(GRU)-0.253. In the Dongsi station according to MAE score and R\({}^{2}\), the best performance was 1-step AAQP (GRU)-41.468 and 0.228 respectively, which confirmed that proposed AAQP (GRU) method was better compared to other methods. Related to the steps analysis, the results showed that 12-step AAQP was the best. The authors also compared training and prediction time for each model. The results showed that training time (s) of 12-step AAQP (GRU) and the prediction time of 12-step AAQP (LSTM) had better performances. Concerning future work, it was suggested to work on spatial attention and to collect more weather forecast data. _An End-to-End Adaptive Input Selection With Dynamic Weights for Forecasting Multivariate Time Series [18]_: presents the Adaptive Input Selection with Recurrent Neural Network (AIS-RNN) for multivariate time series forecasting.