Flight Delay Prediction using Neural Fusion Network

Predicting flight delays is an essential task in aviation management that aims to improve customer happiness and operational efficiency by foreseeing delays. Our Neural Fusion Networks (NFN) technique, which blends long short-term memories (LSTM) and recurrent neural network, or RNN, architectures, provides a novel approach to flight delay prediction.Utilizing past flight data, meteorological trends, and other pertinent characteristics, the NFN model exhibits exceptional precision, attaining a 91% prediction accuracy. The NFN technique enables robust and consistent delay predictions by capturing both short-term dependencies and long-term trends in flight data by integrating LSTM and RNN capabilities. This study advances predictive analytics in aviation by providing airlines and airports with an effective tool to proactively control flight delays and enhance overall operational efficiency.