Žilinská univerzita v Žiline

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Start date: 2020Subject: search.filters.subject.prediction

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    Forecasting flight delays using machine learning
    (University of Žilina, 2024) Soloviov, Mykhailo; Badánik, Benedikt
    This article considers machine learning and its utilization in the domain of air transportation. The first part of this research aims to define machine learning, describe its historical development and helps delineate machine learning in a broader framework of other data analysis approaches such as artificial intelligence, deep learning and data science. In the second part, the research is meant to explain what machine learning is, tell more about the types of machine learning and how it is used in different scenarios in the aviation industry. This part of the thesis discusses certain areas and real examples of how machine learning is used by multiple companies (aircraft manufacturers) as well as examines the available conclusions of researches already undertaken in the area and determines their connection to the current one. Finally, the practical part of the thesis uses the collected real-time data about departures from two American airports, analyses it with the help of statistical Python-based tools, describes the developed machine-learning algorithm to predict delays, runs experiments on data and discusses results.
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    DETEKCIA A PREDIKCIA HROZBY NÁMRAZY NA ZÁKLADE ANALÝZY POVETERNOSTNEJ SITUÁCIE A SPRÁV O POČASÍ NA LETISKU ŽILINA
    (Žilinská univerzita v Žiline, 2020) Fodor, Pavol; Jarošová, Miriam
    The aim of the paper was to introduce the issue of icing as a dangerous phenomenon in aviation, its effect and impact on the safety of air transport and to approach this dangerous phenomenon in terms of aviation meteorology. We also paid considerable interest to air accidents caused by icing. However, we primarily focused on the description of the different types of icing, its division and occurrence of icing throughout various weather situations. In addition, we paid attention to classify and evaluate the occurrence of the icing on a specific weather situation, which made it possible to effectively predict the occurrence, origin or intensity of icing. We have also introduced the current state of icing prediction and prevention of flight into meteorological conditions with danger of icing.