Spatial disorientation (SD) is a critical factor contributing to accidents in general aviation (GA), posing significant challenges to flight safety. This study conducts a comprehensive data analysis o..
Spatial disorientation (SD) is a critical factor contributing to accidents in general aviation (GA), posing significant challenges to flight safety. This study conducts a comprehensive data analysis of general aviation crash reports to investigate the patterns and underlying factors associated with spatial disorientation. The dataset, derived from official reports from the National Transportation and Safety Board, has over 800 cases to study. Utilizing advanced statistical methods and machine learning algorithms, we analyze the textual data from official accident reports to extract and categorize instances of spatial disorientation. The methodology involves natural language processing (NLP) techniques to systematically review and classify the narratives and findings within the reports, focusing on keywords and phrases indicative of SD. Additionally, we correlate these instances with various factors such as weather conditions, flight phase, pilot experience, and aircraft type to uncover significant trends and risk factors. This study contributes to the ongoing efforts to enhance GA flight safety by providing a data-driven understanding of spatial disorientation accidents.
