Akademik Veri Yönetim Sistemi
EXPERT SYSTEMS WITH APPLICATIONS, cilt.315, 2026 (SCI-Expanded, Scopus)
Wind shear (WS) events are meteorological phenomena that pose significant hazards to aviation operations, particularly during aircraft takeoff and landing. The Low-Level Wind Shear Alert System (LLWAS) is a WS detection system deployed at airports by combining at least two measurement technologies-such as Light Detection and Ranging (LIDAR), Radio Detection and Ranging (RADAR), and anemometers. Owing to its high economic requirements, LLWAS remains relatively rare. In this study, we developed a novel method that can both detect and forecast WS events-specifically, microbursts (MB), sea breezes (SB), gust fronts (GF), and wake vortices (WV) -similar to LLWAS. Enhanced by deep learning (DL), the software not only identifies WS events in real time but also generates predictions for future occurrences. This study can detect WS by implementing modifications to currently used airport weather observation systems, without requiring additional hardware. It should be noted that weather observation is mandatory at airports. However, LLWAS (Low-Level Wind Shear Alert System) systems are not compulsory. Using AWOS data from June at Antalya Airport, we compared our system's detections and forecasts with LLWAS alerts. The findings show that, alongside LIDAR and RADAR, the newly developed software is highly effective at detecting low-level WS. All studies have been conducted in accordance with the International Civil Aviation Organisation (ICAO) and Federal Aviation Administration (FAA) WS definitions and rules. This approach promises substantial economic benefits by either replacing or augmenting existing LLWAS installations.