Identifying Human Factors in Aviation Accidents with Natural Language Processing and Machine Learning Models

Abstract

The use of machine learning techniques to identify contributing factors in air incidents has grown significantly, helping to identify and prevent accidents and improve air safety. In this paper, classifier models such as LS, KNN, Random Forest, Extra Trees, and XGBoost, which have proven effective in classification tasks, are used to analyze incident reports parsed with natural language processing (NLP) techniques, to uncover hidden patterns and prevent future incidents. Metrics such as precision, recall, F1-score and accuracy are used to assess the degree of correctness of the predictive models. The adjustment of hyperparameters is obtained with Grid Search and Bayesian Optimization. KNN had the best predictive rating, followed by Random Forest and Extra Trees. The results indicate that the use of machine learning tools to classify incidents and accidents helps to identify their root cause, improving situational decision-making.