Generative Augmentation of Imbalanced Flight Records for Flight Diversion Prediction: A Multi-objective Optimisation Framework

TL;DR

This paper introduces a multi-objective optimisation framework to enhance flight diversion prediction by generating synthetic data with deep generative models, significantly improving model accuracy for rare aviation events.

Contribution

It presents a novel hyperparameter optimisation approach for deep generative models to augment imbalanced flight data, boosting predictive performance for flight diversions.

Findings

Optimised generative models produce high-quality synthetic diversion data.

Synthetic data augmentation improves diversion prediction accuracy.

The framework outperforms non-optimised models and statistical baselines.

Abstract

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.