Optimal-Transport-Based Positive and Unlabeled Learning Method for Windshear Detection

Windshear is a microscale meteorological phenomenon that can be dangerous to aircraft during the take-off and landing phases. Accurate windshear detection plays a significant role in air traffic control. In this paper, we aim to investigate a machine learning method for windshear detection based on previously collected wind velocity data and windshear records. Generally, the occurrence of windshear events are reported by pilots. However, due to the discontinuity of flight schedules, there are presumably many unreported windshear events when there is no flight, making it difficult to ensure that all the unreported events are all non-windshear events. Hence, one of the key issues for machine-learning-based windshear detection is determining how to correctly distinguish windshear cases from the unreported events. To address this issue, we propose to use a positive and unlabeled learning method in this paper to identify windshear events from unreported cases based on wind velocity data collected by Doppler light detection and ranging (LiDAR) plan position indicator (PPI) scans. An optimal-transport-based optimization model is proposed to distinguish whether a windshear event appears in a sample constructed by several LiDAR PPI scans. Then, a binary classifier is trained to determine whether a sample represents windshear. Numerical experiments based on the observational wind velocity data collected at the Hong Kong International Airport show that the proposed scheme can properly recognize potential windshear cases (windshear cases without pilot reports) and greatly improve windshear detection and prediction accuracy.