Innovations in Self-Managing Systems: Big Data to Avoid Weather Related Flight Delays

Innovations in Self-Managing Systems: Big Data to Avoid Weather Related Flight Delays

Mrs. Pallavi N R                                                                                Ms.Meghana.R

Professor                                                                                              Student

Computer Science and Engineering                                             Computer Science and Engineering

BGS Institute of Technology ,                                                         BGS Institute of Technology,

Adichunchanagiri University,                                                            Adichunchanagiri University,

Faculty of engineering management and technology             BG Nagara, Karnataka

BG Nagara, Karnataka

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Abstract - Harnessing big data for weather-related flight delays involves gathering real-time data from meteorological agencies, airports, airlines, and historical flight databases. This data is amalgamated for analysis, leveraging predictive analytics and machine learning to forecast delay probabilities based on weather conditions. A centralized platform constructs a risk assessment framework, evaluating factors like severity, duration, airport capacity, and aircraft capabilities. Proactive suggestions are then offered to airlines, airports, and air traffic controllers, including schedule adjustments, flight rerouting, operational optimization, and improved passenger communication. Continuous monitoring and refinement ensure responsiveness to changing weather dynamics and operational needs, reducing the impact of weather-induced disruptions on air travel. This comprehensive approach enhances efficiency and reliability, minimizing delays and enhancing overall passenger experience.

Key Words:  Aggregation, Analysis, Prediction, Risk assessment, Recommendations, Optimization.