## 3 Results
### Exhaustive Descriptions of Included Studies
#### 3.2.1 Group 1: Neural Network (NN)
The model consisted of two parts; the first model generated context-dependent importance weights for selecting proper inputs; afterwards, the second model based on the inputs predicted the target variable. For the latter part Elman networks (simple RNN), LSTM and GRU were applied. RMSE, MAE and MAPE were applied to estimate model performances. The dataset used in this research consisted of 3 different types: financial, energy use of appliances and air quality dataset. The latter one was taken from the machine learning repository at UCI [19]. For comparison purposes the following methods were taken: LSTM, GRU, RNN, SVM, RF, AdaBoost, DT based on Recursive Feature Elimination, VAR-based and without anything, and also LSTM, RNN, GRU based on AIS-RNN. The results showed that the proposed model outperformed other models. As future work, the authors proposed to extend AIS-RNN as an end-to-end ensemble model. _Prediction of Urban PM\({}_{2.5}\) Concentration Based on Wavelet Neural Network [20]_: focuses on prediction of PM\({}_{2.5}\) using Wavelet Neural Network (WNN). Different techniques were chosen in order to evaluate the effectiveness of WNN, including ELM, Fuzzy Neural Network (FNN) and Least Squares Support Vector Machine (LSSVM). The dataset contained hour average concentration of temperature, relative humidity, O\({}_{3}\), CO, NO\({}_{2}\), SO\({}_{2}\), PM\({}_{10}\) and PM\({}_{2.5}\). Firstly, the Pearson correlation and the bilateral significance test were used to calculate the correlation between PM\({}_{2.5}\) and other pollutants. After inputting PM\({}_{10}\), CO, NO\({}_{2}\), SO\({}_{2}\) and O\({}_{3}\), temperature and relative humidity were considered for predicting the concentration of PM\({}_{2.5}\). For evaluating the prediction models, the statistical parameter of R\({}^{2}\), RMSE, and MAPE were chosen. Comparing to other methods, the results showed that detection results based on WNN were more accurate, only in terms of R\({}^{2}\) for 1 hour the FNN had comparatively better results (0.099). However, making WNN still is challenging because of the determination of proper wavelet basis function and hidden layer nodes. _A Deep Belief Network Based Model for Urban Haze Prediction [21]_: [PERSON] et al. proposed a Deep Belief Network (DBN) model to improve urban haze prediction (DBN-based urban haze prediction: DBN-H). Multilayer restricted Boltzmann machines and a single-layer back propagation network were applied. For meteorological data prediction, competitive adaptive-reweighed method was applied. For evaluation purposes were used R and MAE. In terms of haze content, PM\({}_{2.5}\) and PM\({}_{10}\) were taken, and in terms of meteorological content, wind speed, wind direction, temperature, humidity, light, and atmospheric pressure were obtained for the period of 2016-2017. Multiple regression, ARMA, Classification and Regression Tree, and NN were applied for comparison purposes. The results showed that DBN-H outperformed other methods, having R-0.767 and MAE-26.5 \(mug/\)m\({}^{3}\) results. Overall,DBN-H model provided a correlation result with 18% better than others, and MAE was decreased by 15.7 \(\mu\)g/m\({}^{3}\). As a limitation, the lack of data was reported. _Deep Distributed Fusion Network for Air Quality Prediction [22]_: [PERSON] et al. offered the Deep Neural Network (DNN)-based approach consisted of a spatial transformation component and a deep distributed fusion network. The model was applied for 48 hour fine-grained air quality forecasts for more than 300 Chinese cities. Air quality data consisted of hourly collected elements, including PM\({}_{2.5}\), PM\({}_{10}\), NO\({}_{2}\), CO, O\({}_{3}\), and SO\({}_{2}\). A meteorological dataset consisted of weather (sunny, cloudy, overcast, foggy, snow, small rain, moderate rain, and heavy rain), humidity, temperature, pressure, wind speed, and wind direction was used. Weather forecast dataset consisted of weather, temperature, wind strength and wind direction.