航班到港延误时长预测及特征分析

为破除XGBoost模型的黑盒特性，增强模型的说服性，提出一种基于SHAP的可解释性航班到港延误时长预测模型。首先，对航班历史数据、天气数据进行融合，在融合数据的基础上进行异常值处理，并利用递归特征消除方法进行特征选择；其次，构建航班延误时长预测模型，利用遗传算法进行参数调优，并与目前常用的模型进行对比；最后，在航班延误时长预测的基础上结合SHAP模型，从总体特征和特征间的相互关系2个角度分析特征的重要程度。实验结果表明，经过遗传算法调优的XGBoost模型预测精度更高，其中MAE降低了8.94%，RMSE降低了19.85%，MAPE降低了6.15%，且其模型精度更高。因此，SHAP模型破除了XGBoost模型的黑盒特性，增强了模型的可解释性，可为降低航班延误时长提供技术支持。

Prediction and characteristic analysis of flight arrival delay

To break the black box feature of XGBoost model and enhance its persuasiveness, an interpretable flight delay prediction model based on SHAP was proposed. Firstly, based on the fusion of flight history data and weather data, outliers were processed and features were selected by recursive feature elimination method. Secondly, a flight delay duration prediction model was constructed, and genetic algorithm was used for parameter optimization, then it was compared with commonly used models at present. Finally, based on the prediction of flight delay duration and the SHAP model, the importance of features was analyzed from two perspectives: overall features and the interrelationships between the features. The experimental results show that the XGBoost model optimized by genetic algorithm has higher prediction, with a decrease of 894% in MAE, 1985% in RMSE, and 615% in MAPE, with higher accuracy compared to other models. The SHAP model can break the black box characteristics of the XGBoost model and enhance its interpretability, which provides some support for reducing flight delay duration.