A Fuzzy-Enhanced Explainable AI Framework for Flight Continuous Descent Operations Classification

TL;DR

This paper introduces a fuzzy-enhanced explainable AI framework for classifying flight continuous descent operations, combining machine learning, fuzzy logic, and SHAP analysis to produce interpretable models with high accuracy, aiding operational decision-making.

Contribution

The study develops a novel FEXAI framework that integrates fuzzy logic with machine learning and SHAP for transparent classification of CDO adherence, addressing explainability in aviation AI models.

Findings

Models achieved over 90% accuracy in classifying CDO adherence.

Key predictors include descent rate, number of descent segments, and heading changes.

FEXAI provides human-readable rules for operational decision support.

Abstract

Continuous Descent Operations (CDO) involve smooth, idle-thrust descents that avoid level-offs, reducing fuel burn, emissions, and noise while improving efficiency and passenger comfort. Despite its operational and environmental benefits, limited research has systematically examined the factors influencing CDO performance. Moreover, many existing methods in related areas, such as trajectory optimization, lack the transparency required in aviation, where explainability is critical for safety and stakeholder trust. This study addresses these gaps by proposing a Fuzzy-Enhanced Explainable AI (FEXAI) framework that integrates fuzzy logic with machine learning and SHapley Additive exPlanations (SHAP) analysis. For this purpose, a comprehensive dataset of 29 features, including 11 operational and 18 weather-related features, was collected from 1,094 flights using Automatic Dependent Surveillance-Broadcast (ADS-B) data. Machine learning models and SHAP were then applied to classify flights' CDO adherence levels and rank features by importance. The three most influential features, as identified by SHAP scores, were then used to construct a fuzzy rule-based classifier, enabling the extraction of interpretable fuzzy rules. All models achieved classification accuracies above 90%, with FEXAI providing meaningful, human-readable rules for operational users. Results indicated that the average descent rate within the arrival route, the number of descent segments, and the average change in directional heading during descent were the strongest predictors of CDO performance. The FEXAI method proposed in this study presents a novel pathway for operational decision support and could be integrated into aviation tools to enable real-time advisories that maintain CDO adherence under varying operational conditions.