面向恶劣天气的航班延误Stacking集成预测模型  

Flight delay stacking ensemble prediction model for severe weather

作　　者：孙玥 丁建立[2] SUN Yue;DING Jianli(The Management Center of Ninghe Campus,Civil Aviation University of China,Tianjin 300300,China;College of Computer Science and Technology,Civil Aviation University of China,Tianjin 300300,China)

机构地区：[1]中国民航大学宁河校区管理中心,天津300300 [2]中国民航大学计算机科学与技术学院,天津300300

出　　处：《大数据》2025年第2期152-166,共15页Big Data Research

基　　金：国家自然科学基金民航联合基金重点项目(No.U2033205,No.U2233214)。

摘　　要：天气因素作为影响航班延误的首要因素,对航班延误预测有重要影响。面向恶劣天气,对航班延误时长进行多分类预测,并针对传统单一模型预测精度低、稳定性差等问题,提出一种基于Stacking的航班延误集成预测模型,融合航班数据与天气数据特征,采用LightGBM、XGBoost等多个异质分类器作为基学习器,SVM作为元学习器,构建堆叠式的双层集成学习框架。为验证模型有效性,构建多个单一模型与集成模型进行比较。实验结果证明,Stacking集成预测模型性能最优,总体准确率达到95.25%,F1分数达到0.9527。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.

关 键 词：航班延误预测 Stacking集成学习 多模型融合 恶劣天气 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]