Aviation Safe Landing Prediction Utilizing Long-Short Term Memory Algorithm

The aviation industry is significantly dependent on sophisticated technology, such as engines, avionics, and air traffic control systems, to assure the safety and efficiency of its operations. A significant proportion of commercial aviation accidents may have been averted by implementing a flying procedure wherein an incoming aircraft discontinues its landing attempt and reenters the queue for landing. The timely implementation of this approach can substantially impact mitigating the overall accident rate within the aviation industry. Hence, the main aim of this article is to effectively forecast flight landings, improve safety measures and enable informed decision-making in complex situations for both autonomous and human-operated aircraft using a Deep Learning algorithm such as long short term memory (LSTM). The performance of LSTM compared with machine learning (ML) technique like Support Vector Machines (SVM) and logistic regression algorithm in terms of sensitivity and specificity