Date of Award
3-2025
Document Type
Thesis
Degree Name
Master of Science
Department
Department of Operational Sciences
First Advisor
Neil C. Ranly, PhD
Abstract
This study applies advanced Machine Learning (ML) to Flight Data Recorder (FDR) data for fuel consumption predictions. It explores feature engineering, model selection, and Hyper-Parameter Optimization (HPO) across all flight phases. Baseline models like Ordinary Least Squares (OLS) regression, Multi- Layer Perceptrons (MLPs), and decision trees are compared to Deep Neural Networks (DNNs), Recurrent Neural Networks (RNNs) with Gated Recurrent Unit (GRU) layers, and XGBoost. Results analyze segmentation strategies, tailored features, and model performance. A counterfactual analysis compares ML models to operational fuel predictions, demonstrating their deployment potential. Findings establish a foundation for future ML-driven advancements in aviation fuel optimization.
AFIT Designator
AFIT-ENS-MS-25-M-172
Recommended Citation
Levandowski, Adam C., "Machine Learning with Flight Data Recorder Data for Flight Fuel Consumption Predictions" (2025). Theses and Dissertations. 8238.
https://scholar.afit.edu/etd/8238
Included in
Aviation Commons, Data Science Commons, Operational Research Commons
Comments
An embargo was observed for posting this thesis.
This work is marked Distribution A, Approved for Public Release. PA case number 88ABW-2025-0314