Analysis and Predictive Modeling of Stall and Spin Accidents in General Aviation Fixed-Wing Aircraft

Stall and spin accidents represent a persistent safety concern in General Aviation, with approximately 41% resulting in fatalities despite their relatively low occurrence. This study analyzes 1,079 General Aviation stall and spin accidents from 2008 to 2023 using the National Transportation Safety Board accident database. Our analysis identified the top phases of flight, injury outcomes, and causal factors in stall/spin accidents. Findings indicate that over 93% of stall/spin events involved General Aviation aircraft, with most accidents occurring during initial climb, takeoff, or low-altitude maneuvering. Pilot-related control errors, especially poor airspeed and angle-of-attack management, were the most common causes. To assess accident severity prediction, we trained machine learning models, Logistic Regression, Decision Tree, Random Forest, and XGBoost—on structured features. The Random Forest model achieved the best performance (accuracy = 0.81, ROC-AUC = 0.89), demonstrating potential for integration into safety monitoring and training systems. The findings offer an interpretable, data-driven framework for identifying high-risk scenarios and informing targeted training to prevent stall/spin accidents in General Aviation.