The incorporation of AI into the aviation industry is far from a new innovation; on the contrary, it has been around for decades—and for good reason, as the aviation industry is by far one of the most complex and fastest-growing industries in the entire world. So, naturally, AI also requires continuous optimization and improvement to be effective in this rapidly-changing environment when it comes to assisting the control tower operators, especially during the landing phase. The main objective of this project is to optimize the AI models to assist operators in making real-time, impactful decisions while providing explanations behind the output in different landing emergencies such as scheduling conflicts in congestions and bad weather conditions that make landings dangerous. We aim to use highly effective and adaptable algorithms to solve these issues such as Multi-Agent Deep Reinforcement Learning for dynamic scheduling, Distributed Systems to handle large workload volumes, Explainable AI to justify the model’s output, and Gradient Boosting to serve as a baseline algorithm for comparison against our algorithms’ performance to clearly show our contribution. The database will consist of diverse flight datasets to form a solid foundation for the models to train on, thus providing accurate results. Simulators will be employed to evaluate the models’ performance across various scenarios that push the AI to its limits to demonstrate what it’s truly capable of.

• Implement Distributed Systems in the model’s architecture to achieve load balancing, to enable real-time adaptability under large workload volumes to prevent performance degradation.

• Increase the model’s adaptability and scalability to handle rare and extreme emergencies through the use of Multi-Agent Deep Reinforcement Learning.

• Incorporate Explainable AI(XAI) in the development stage of the model all the way to the deployment stage instead of integrating it after the model has already been deployed.

• Use LLM APIs in Explainable AIs to produce easy-to-understand explanations for non-technical employees.

• Teach the model to use time-saving scheduling methods instead of First-Come-First-Served rule to decrease overall landing time.

• Showcase the scenario of what if system suggestions using a comprehensive graphical simulation, which will help airport operators easily interpret the model’s output and validate it.

• Implementing cyber security measures to prevent attacks into the sensitive nature of airports’ environments.

The system aims to help tower controllers and operators manage the landing emergencies with both security and efficiency. In order to fulfill its role, the system will be able to do the following:

• Interpret the given historical data and extract useful features.

• Provide useful real-time suggestions to assist control tower operators in resolving complex and urgent situations.

• Operate effectively and reliably without degradation under hard conditions and large workload volumes.

• Explain the model’s process and reasons behind any given suggestion.

• Test the outcomes of the model’s suggestions using simulations and historical data.

• Ensure safety against attacks by applying reliable cyber security measures.