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Aviation Forecasting Using Machine Learning: Predicting ATC Costs and Fuel Consumption
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Orientador(es)
Resumo(s)
Air Traffic Control costs and Fuel expenses are two of the main expenses of airline companies.
Predicting these two variables is essential for the Flight Operations department, as there is a
need to calculate the budget for each one and communicate it to the financial department.
This master thesis explores the use of machine learning models to predict these expenses,
aiming to reduce the dependency on other departments and obtain faster, accurate
predictions. The CRISP-DM methodology was adopted to reach the final models for each
target variable. Although various machine learning models were trained and tested, Hist
Gradient Boosting was the best performing model for each target, however, the results did
not meet the expectations. This approach, with the given dataset, performs worse than the
company’s existing method for predicting these variables. The main conclusion is that there is
a need to engineer existing features and add more relevant ones into the training of the
models to fully realize the potential of this approach.
Descrição
Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business Intelligence
Palavras-chave
Machine Learning ATC Costs Fuel Consumption Predictive Modeling Aviation Forecast SDG 8 - Decent work and economic growth SDG 9 - Industry, innovation and infrastructure