Search papers, labs, and topics across Lattice.
This paper addresses the challenge of imbalanced data in flight diversion prediction by using generative models to augment rare diversion events. They propose a multi-objective optimization framework to tune Tabular VAE, CTGAN, and CopulaGAN models for generating synthetic flight diversion records. Results show that optimized generative models significantly improve the accuracy of flight diversion prediction models compared to those trained only on real data, demonstrating the value of synthetic data augmentation for rare event prediction.
Overcome the scarcity of rare flight diversion events by using optimized generative models to create synthetic data that substantially improves prediction accuracy.
Flight diversions are rare but high-impact events in aviation, making their reliable prediction vital for both safety and operational efficiency. However, their scarcity in historical records impedes the training of machine learning models utilised to predict them. This study addresses this scarcity gap by investigating how generative models can augment historical flight data with synthetic diversion records to enhance model training and improve predictive accuracy. We propose a multi-objective optimisation framework coupled with automated hyperparameter search to identify optimal configurations for three deep generative models: Tabular Variational Autoencoder (TVAE), Conditional Tabular Generative Adversarial Network (CTGAN), and CopulaGAN, with the Gaussian Copula (GC) model serving as a statistical baseline. The quality of the synthetic data was examined through a six-stage evaluation framework encompassing realism, diversity, operational validity, statistical similarity, fidelity, and predictive utility. Results show that the optimised models significantly outperform their non-optimised counterparts, and that synthetic augmentation substantially improves diversion prediction compared to models trained solely on real data. These findings demonstrate the effectiveness of hyperparameter-optimised generative models for advancing predictive modelling of rare events in air transportation.
