DEEP LEARNING APPROACH FOR UNSTABILIZED APPROACH PREDICTION BASED ON ENERGY MANAGEMENT CRITERIA - Lintang Kinanthi, Diandra
Accidents in flight operations mostly occur during the approaching phase, despite this phase being the shortest among all flight phases. Accidents during the approaching and landing phases contribute to 46% of the total aviation accidents caused by various factors. A prevalent issue leading to accidents during the final approach phase is the occurrence of an Unstabilized Approach (UA). In essence, UA can be defined as a situation where a safe landing is not achieved. Identifying whether a flight is experiencing UA can be accomplished through various methods, one of which involves utilizing energy management criteria. This method will be employed in the forthcoming research. To minimize UA cases, early prediction is essential, allowing pilots ample time to react and maintain aircraft stability when an impending UA scenario is predicted. This study employs deep learning to facilitate prediction. Deep learning is a subset of machine learning that employs layered algorithmic structures known as Artificial Neural Networks (ANNs). This approach enables the real-time prediction of UA incidents during flights.