Algorithm for Airline Error Delays Prediction to Enhanced Predictive Accuracy

Flight delays have significant implications on airline efficiency and customer satisfaction. Existing prediction models often struggle with accuracy due to the complexity, volume, and noisiness of flight-related data. This study proposes an advanced predictive model using Deep Learning (DL), specifically a Stacked Denoising Autoencoder combined with the Levenberg-Marquardt (LM) algorithm (SDA-LM). The model leverages features such as flight time duration and previous flight delays. Comparative analysis with SAE-LM and SDA models using both balanced and imbalanced datasets shows the SDA-LM model achieves superior precision, accuracy, sensitivity, and F-measure. Experimental results on U.S. domestic airline datasets demonstrate that SDA-LM outperforms traditional methods including RNN in delay prediction.