Fuel consumption prediction of civil air crafts using deep learning: a comparative study

Abstract

Accurate fuel consumption prediction is critical for minimizing the adverse impact of fuel emissions on the environment, conserving fuel, and reducing flight costs. Additionally, precise fuel forecasting enhances trajectory prediction and supports effective air traffic management. This study evaluates the predictive performance of two deep learning techniques in predicting the fuel consumption of a civil aircraft belonging to Airbus A320NEO. Based on the analysis, the findings show that the deep neural network (DNN) model has better score of indicators and than the recurrent neural network (RNN) including mean absolute error (MAE), mean squared error (MSE), root mean squared error (RMSE) and R-squared (R2). By integrating an automated feature selection approach with an optimized deep learning framework, this research contributes to the development of a robust and efficient predictive system for fuel consumption. The findings have practical implications for improving fuel management strategies in aviation, leading to cost savings and reduced emissions. One limitation of this study is its reliance on specific environmental variables, which may limit the model’s generalizability across different flight conditions, aircraft types, and operational scenarios.