Integrated Dual LSTM Model-Based Air Quality Prediction

Although air quality prediction is a crucial tool for weather forecasting and air quality management, algorithms for making predictions that are based on a single model are prone to overfitting. In order to address the complexity of air quality prediction, a prediction approach based on integrated dual long short-term memory (LSTM) models was developed in this study. The model takes into account the variables that affect air quality such as nearby station data and weather information. Finally, two models are integrated using the eXtreme Gradient Boosting (XGBoosting) tree. The ultimate results of the prediction may be obtained by summing the predicted values of the ideal subtree nodes. The proposed method was tested and examined using five evaluation techniques. The accuracy of the prediction data in our model has significantly increased when compared with other models.