AI-Driven Flight Delay Prediction Model for Climate-Resilient Airspace Management
Project Title
AI-Driven Flight Delay Prediction Model for Climate-Resilient Airspace Management
Problem
This project researches the use of machine learning and artificial intelligence to predict weather-induced flight delays and improve climate resilience in airspace management. Using historical flight data, real-time weather reports, and air traffic information, students will develop AI-driven predictive models (Random Forest, XGBoost, and LSTMs) to estimate potential disruptions. The project will also include a literature review on AI applications in aviation, feature engineering for improved model accuracy, and a visualization dashboard for real-time delay forecasting.
Approach
This project researches the use of machine learning and artificial intelligence to predict weather-induced flight delays and improve climate resilience in airspace management. Using historical flight data, real-time weather reports, and air traffic information, students will develop AI-driven predictive models (Random Forest, XGBoost, and LSTMs) to estimate potential disruptions. The project will also include a literature review on AI applications in aviation, feature engineering for improved model accuracy, and a visualization dashboard for real-time delay forecasting.
Expected Outcome
-Success for the undergraduate student in this research project means developing a solid understanding of machine learning techniques.
-Gaining hands-on experience in applying AI tools to real-world datasets.
-Contributing to a meaningful, data-driven solution for climate resilience in aviation.
-By the end of the project, students should be able to build, evaluate, and explain predictive models and communicate their findings clearly through visualizations and written reports.
Final Report: View PDF