Delays in air travel continue to disrupt aviation operations, causing passenger inconveniences and economic losses. This research proposes a flight delay prediction framework using a random forest model enhanced with carefully engineered features from flight and weather data. Key features introduced include historical delay metrics and network centrality attributes designed to capture flight network dependencies. The proposed model achieves remarkable predictive performance, with 92.36% accuracy, 98.29% precision, 86.22% recall, 91.86% F1 score, and 96.78% AVC-ROC. These results demonstrate the potential of combining Random Forest with domain-specific features to predict flight delays effectively.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Flight Delay Prediction Using Random Forest with Enhanced Feature Engineering


    Beteiligte:
    Afrane, Mary Dufie (Autor:in) / Xu, Yao (Autor:in) / Li, Lixin (Autor:in) / Wang, Kai (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    22.03.2025


    Format / Umfang :

    164555 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Flight Delay Prediction Using Random Forest Classifier

    Rahul, R. / Kameshwari, S. / Pradip Kumar, R. | Springer Verlag | 2021


    Research on Flight Delay Prediction Based on Random Forest

    Hu, Peng / Zhang, Jianping / Li, Ning | IEEE | 2021


    Flight Delay Prediction Using Machine Learning Techniques

    Tijil, Yash / Dwivedi, Nripendra / Srivastava, Satyam Kumar et al. | IEEE | 2024


    Train delays prediction based on feature selection and random forest

    Ji, Yuanyuan / Zheng, Wei / Dong, Hairong et al. | IEEE | 2020


    Flight Arrival Delay Prediction Using Deep Learning

    Sharma, Nishant / Vijayalakshmi, S. | IEEE | 2024