Flight delay stacking ensemble prediction model for severe weather

Abstract

Weather factors, as the primary factors affecting flight delays, have an important impact on flight delay prediction. Confronting the severe weather, multi-classification prediction of flight delay duration was made, and a Stacking-based integrated flight delay prediction model was proposed for the problems of low prediction accuracy and poor stability of traditional single model. Combining flight data and weather data features, multiple heterogeneous classifiers such as LightGBM and XGBoost were used as base learners, and SVM was used as the primary learner. A stacked, two-layer integrated learning framework was constructed. To verify the model validity, multiple single models were constructed for comparison with the integrated model. The experimental results demonstrate that the Stacking integrated prediction model has the best performance with an overall accuracy of 95.25% and an F1 score of 0.9527.

Keywords

• Stacking ensemble learning
• multi model fusion
• severe weather