Interpretability analysis of flight delay prediction based on KernelSHAP

Hancheng Li; Jingyi Qu

doi:10.1117/12.3052928

3 January 2025 Interpretability analysis of flight delay prediction based on KernelSHAP

Hancheng Li, Jingyi Qu

Author Affiliations +

Proceedings Volume 13442, Fifth International Conference on Signal Processing and Computer Science (SPCS 2024); 134420I (2025) https://doi.org/10.1117/12.3052928
Event: Fifth International Conference on Signal Processing and Computer Science (SPCS 2024), 2024, Kaifeng, China

Abstract

In order to enhance the interpretability of deep learning models and improve the credibility of model predictions, we propose an interpretability analysis method for flight delay prediction based on KernelSHAP. Delay prediction uses flight and weather data, and the ATMAP algorithm is used to generate weather condition scores that are strongly correlated with flight delay conditions to enrich features. The deep learning model NR-DenseNet is selected for delay prediction. KernelSHAP is combined to analyze the input features and the decision-making process of the model from two perspectives: feature analysis of overall samples and feature analysis of single sample. The results show that the addition of KernelSHAP enhances the interpretability of the model, effectively breaks the black box characteristics of the model, and can provide professionals with more reliable decision-making guidance.

(2025) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Hancheng Li and Jingyi Qu "Interpretability analysis of flight delay prediction based on KernelSHAP", Proc. SPIE 13442, Fifth International Conference on Signal Processing and Computer Science (SPCS 2024), 134420I (3 January 2025); https://doi.org/10.1117/12.3052928

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