FLIGHT ANOMALY DETECTION

This study develops a machine learning model to predict abnormalities in commercial airplanes using real-world Automatic Dependent Surveillance-Broadcast (ADS-B) data, focusing on altitude changes exceeding 100 feet in 10 seconds. Following the methodology established by Passarella et al. (2024), this research implements and compares 25 different machine learning algorithms, ultimately selecting Quadratic Discriminant Analysis (QDA) as the optimal approach. The dataset comprises 167,844 records, including 84,074 normal and 83,770 abnormal instances, with features such as altitude, velocity, heading, latitude, and longitude. The theoretical foundation covers the comprehensive taxonomy of machine learning methods, from supervised learning algorithms like Support Vector Machines and Decision Trees to unsupervised approaches such as K-Means clustering. The QDA model achieves superior performance with 93-97% accuracy, 0.96-0.97 ROC-AUC, validated through stratified 5-fold cross-validation. Visualizations, including altitude plots and ROC curves, enhance interpretability for aviation professionals. This research demonstrates that QDA's ability to model non-linear decision boundaries with class-specific covariance matrices makes it particularly suitable for complex aviation data patterns, supporting enhanced flight safety and operational efficiency.