Machine learning-based turbulence-risk prediction method for the safe operation of aircrafts

Abstract

Abstract This study has proposed a method for detecting turbulence, a primary factor that influences safe aircraft operation. The number of observed turbulence events is limited, thereby indicating the requirement of an appropriate flow for detecting turbulence events from a small number of samples. In addition, the opinions and experiences of pilots must be reflected at the initial stage to address the high risk of turbulence occurrence, which can result in airline operations being cancelled. Thus, this study proposed a method for predicting turbulence occurrence based on the turbulence occurrence date information provided by airlines as well as meteorological data sets obtained from open data available in Japan as teacher data. However, because commonly used machine learning methods are unable to detect the turbulence occurrence date, the proposed method employed principal component analysis coupled with the K-Means method to generate risk clusters with a high likelihood of turbulence occurrence and consequently perform statistical checks. Subsequently, the risk clusters were utilized as supervisory data for turbulence occurrence, while the support vector machine was used for predicting turbulence occurrence. Furthermore, the results obtained with the proposed method were statistically checked as well as practically verified by a pilot to confirm the appropriateness of the turbulence occurrence date predicted.

Keywords

• PCA
• k-means
• SVC
• Risk cluster
• Open data
• Meteorological data