A Machine Learning–Based Predictive Framework for Nighttime High-Power Aircraft Engine Run-Up Operations

Nighttime high-power aircraft engine run-ups represent a critical operational activity with potential implications for airport management and environmental assessment; yet, their quantitative prediction remains limited in existing literature. The objective of this study is to develop and evaluate a data-driven machine learning framework capable of accurately predicting nighttime high-power engine run-up characteristics using historical operational data. A supervised learning methodology is adopted using an open-access dataset comprising approximately 860 nighttime run-up records. The proposed framework includes data preprocessing, temporal feature engineering (month, season, and quarter), exploratory data analysis, correlation assessment, and comparative model development using Linear Regression, Random Forest, Gradient Boosting, Extreme Gradient Boosting (XGBoost), and an Artificial Neural Network (multilayer perceptron). Model performance is evaluated using coefficient of determination (R²), root mean square error (RMSE), mean absolute error, and mean absolute percentage error, supported by predicted-versus-actual comparisons and learning-curve analysis. The results demonstrate that ensemble-based models outperform linear and neural network approaches, with XGBoost achieving the highest predictive accuracy on the testing dataset (R² ≈ 0.90, RMSE ≈ 2.15). Learning curves further indicate stable validation performance with increasing training data size. The proposed machine learning framework provides a structured and data-driven approach for predicting nighttime high-power aircraft engine run-up activity within the analyzed operational dataset, offering a valuable analytical tool for data-driven assessment of airport ground operations.