A Novel Combined Model for Short-Term Emission Prediction of Airspace Flights Based on Machine Learning: A Case Study of China

In order to improve the capability of situational awareness and operational efficiency by considering environmental impact, a prediction model for short-term flight emissions within en route airspace is proposed in this paper. First, the measurement method of fuel consumption and flight emissions based on actual meteorological data is established, and the pattern of flight emissions is analyzed. Then, an adaptive weighting approach is proposed by considering prediction results obtained from a long–short term memory (LSTM) prediction model and extreme gradient boosting (XGBoost) prediction model, respectively. Taking the Guangzhou area control centre (ACC) AR05 sector in central and southern China as an example, the model is trained and tested on emission datasets with three statistical scales, 60 min, 30 min, and 15 min. The result shows that the combined variable–weight prediction model has the greatest prediction effect compared to six other models. In terms of time scale, the prediction performance is best on the 60 min statistical scale dataset; larger statistical unit magnitudes of emissions during the predicting process show better short-term prediction performance. In addition, the increase in data features when training the model plays an essential role in promoting model accuracy. The model established in this paper has high prediction accuracy and stability, which is capable of providing short-term prediction of airspace flight emissions.