Towards dynamic flight separation in final approach: A hybrid attention-based deep learning framework for long-term spatiotemporal wake vortex prediction

The conservative and distance-based static wake vortex-related separation may restrict runway operational efficiency. Recent studies have demonstrated the potential of wake separation reduction under the Re-categorisation scheme of Aircraft Weight (RECAT). Furthermore, dynamic time-based flight separation considering vortex evolution with respect to aircraft pairs and meteorological conditions will be the ultimate objective for improving runway operational capacity without compromising safety. This paper presents a hybrid deep learning framework for aircraft wake vortex recognition, evolution prediction, and preliminary dynamic separation assessment in the final approach. Two-stage Deep Convolutional Neural Networks (DCNNs) are utilised to identify vortex locations and strength from wake images. Subsequently, we propose the Attention-based Temporal Convolutional Networks (ATCNs) for future long-term vortex decay and transport forecasts based on initial vortex information from DCNNs. 17,254 wake sequences generated by arrival flights at Hong Kong International Airport (HKIA) are used in this study. The proposed ATCN models outperform the specific benchmarks. Furthermore, the hybrid DCNN-ATCN model shows great benefits in mining both spatial vortex characteristics and temporal dependencies in vortex evolution, and achieves a computational speed of approximately 7 s per sequence. The final vortex duration assessment demonstrates a significant potential for separation reduction in the final approach when the crosswind speed exceeds 3 m/s. This study provides important implications for online and fast-time wake behaviour monitoring and state estimation. The results of vortex duration analysis conform to the RECAT-EU standards and present an efficient strategy for developing dynamic flight separation systems.