Role of AI in weather prediction, flight planning, route optimization and scheduling.

Open Access
Conference Proceedings
Authors: Dimitrios ZiakkasKonstantinos PechlivanisAbner Flores

Abstract: Artificial intelligence (AI) is being used to improve weather prediction and turbulence detection, which can help to enhance flight safety. AI-powered weather prediction systems can be used to generate more accurate and up-to-date weather forecasts. This information can help pilots make better decisions about flight planning and execution. The turbulence detection systems can identify areas of turbulence in real time. This information can be used to help pilots avoid turbulence, which can help to improve safety and comfort for passengers. Reduced risk AI-powered systems can help to reduce the risk of accidents caused by weather or turbulence by improving accuracy and providing earlier warnings. The National Weather Service (NWS) uses AI to improve weather prediction models. The NWS reports recognize that AI has improved its forecasts' accuracy by up to 10%. Moreover, the Federal Aviation Administration (FAA) uses AI to detect real-time turbulence. The FAA reports that the use of AI applications makes identifying turbulence areas easier and more effective in avoiding overflying into them. Airlines are using AI to improve their flight planning and execution. Airlines following weather AI recommendations reduce delays and improve the safety of their flights. In detail, Weather Prediction AI analyses large volumes of weather data, such as radar, satellite, and numerical weather prediction models, to provide accurate and timely weather forecasts. This helps pilots plan and adjust flight routes and schedules to avoid severe weather conditions and improve flight safety. Turbulence Detection AI applications detect and predict turbulence in real time using sensor data and weather models. This helps pilots avoid turbulence and provides passengers with a smoother and more comfortable flight experience while reducing the risk of injuries. In the long run, Machine Learning develops machine learning algorithms to learn from past weather and turbulence data and predict future conditions more accurately. This allows airlines to make more informed flight routing and scheduling decisions based on the most up-to-date weather information. Additionally, Automated Decision Support AI develops automated decision support tools that provide pilots with real-time weather and turbulence information, allowing them to make informed decisions about flight routing and scheduling and improve flight safety. Overall, using AI in weather prediction and turbulence detection transforms how airlines monitor and manage weather-related risks, making air transport safer, more efficient, and more comfortable for passengers. By providing accurate and timely weather information, airlines can improve flight safety, reduce flight delays, and provide passengers with a more reliable travel experience. Purdue CREATE approach offers a structured analysis and recommendations for implementing AI in weather prediction, flight planning, route optimization, and scheduling.

Keywords: Weather prediction, flight planning, route optimization and scheduling, Artificial Intelligence

DOI: 10.54941/ahfe1004506

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