Applications of Machine Learning Techniques to Aviation Operations: Promises and Challenges

Abstract

There is an increasing interest in applying methods based on Machine Learning Techniques (MLT) to problems in aviation operations. The current interest is based on developments in Cloud Computing, the availability of open software and the success of MLT in automation, consumer behavior and finance involving large databases. This paper reviews the current-state-of-the art in applying MLT to aviation operations, its promises and challenges. Historically aviation operations have been analyzed using physics-based models and provide information for making operational decisions. This paper describes issues to be addressed in applying either model-driven or data-driven methods. Aviation operations involving many decision makers, multiple objectives, poor or unavailable physics-based models and a rich historical database are prime candidates for analysis using data-driven methods. Currently, the application of MLT to aviation operations falls into three categories: (a) based on the lack of a physics-based model, MLT are the favored approach, (b) MLT perform slightly better than methods using physics-based models and (c) comparison of different MLT to the same application. As always, the best approach depends on the task, the physical understanding of the problem and the quality and quantity of the available data.