AI based Human Factor Approach in Predicting Loss of Control Inflight Factors during Initial Climb in General Aviation

Abstract

Human factors are increasingly leading the General Aviation’s (GA) accident causation although the total number of accidents have significantly improved over past few decades. The actions majorly taken so far correspond to reactive safety approaches rather than proactive ones. GA has been neglected a lot in terms of safety and risk mitigation as the fatality rate has been almost constant for many years now. In this research study, the probable causes of GA Loss of Control-In Flight (LOC-I) accidents under Initial Climb (ICL) phase of flight are obtained from National Transportation Safety Board (NTSB). Each accident is classified into one of the 9 LOC-I accident categories defined by International Air Transport Association (IATA). The preprocessed and feature engineered dataset is fed to a Random Forest (RF) model to be trained. The prediction model gives an accuracy and F-1 score of 88% on the test set. Feature importance and SHapley Additive exPlanation (SHAP) analysis of RF model is performed to get the most influencing features on prediction. The most influential features of the RF model vulnerable are connected to the Human Factor Analysis and Classification System (HFACS) to get insights into the most vulnerable HFACS levels.