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

2025-08-20

AI Summary

Prior research lacks a systematic investigation of factors affecting Continuous Descent Operations (CDO) performance, and existing trajectory optimization methods suffer from limited interpretability. Method: This paper proposes FEXAI, an explainable AI framework integrating fuzzy logic, machine learning, and SHAP-based explanation techniques. Leveraging ADS-B data, we construct a 29-dimensional dataset comprising operational and meteorological features to enable CDO classification modeling and feature attribution analysis. Contribution/Results: FEXAI jointly enhances predictive accuracy and model transparency: all models achieve >90% classification accuracy; SHAP analysis identifies descent rate, number of descent segments, and heading change as the top three influential features; and interpretable, human-readable fuzzy rules are automatically generated to support real-time operational decision-making and safety validation. To our knowledge, this is the first work to deliver a high-accuracy, high-transparency, and production-deployable AI decision-support system for CDO in aviation.

Technology Category

• Humans and AI: Explainable AI (XAI) for Human Understanding
• Planning, Routing, and Scheduling: Model-Based Reasoning
• Computer Vision: Interpretability, Explainability, and Transparency

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Security and Privacy: Data transparency and provenance
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization

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.

Problem

• Classifying flight continuous descent operations adherence levels
• Identifying key factors influencing CDO performance metrics
• Developing explainable AI framework for aviation decision support

Innovation

• Fuzzy logic integration with machine learning
• SHAP analysis for feature importance ranking
• Interpretable fuzzy rule-based classifier construction