Print Email Facebook Twitter Machine learning based aircraft arrival / departure registrations Title Machine learning based aircraft arrival / departure registrations Author de Waard, Mike (TU Delft Electrical Engineering, Mathematics and Computer Science) Contributor Meijer, Erik (mentor) van Deursen, Arie (mentor) Gousios, Georgios (mentor) Degree granting institution Delft University of Technology Date 2017-06-04 Abstract The aviation industry is vastly growing, as travelling by air is more common today than it ever was. However due too inefficiency and lack of communication of accurate flight information between airports, congestion and delays are occurring on a daily basis. While Collaborative Decision Making (CDM) is developed by Euro control to address this issue, the problem of transmitting accurate flight information near real time is not yet solved. Adecs Airinfra did a first attempt at automatic landing and departure registration by a fixed rule based algorithm to address this issue. However, this algorithm has limitations that cannot be solved with tweaking and tuning. In this work, we aim to create a replacement based on machine learning models. In this thesis we present the complete process, starting from raw real world data, turning this intolabelled data up to the point where we define a validation method and present the final results. We managed to create a machine learning landing / departure detection system with up to 99% precision and recall for arrivals, and for departures we managed to get a precision of 94% against 98% recall. Subject Machine LearningTime series classificationAviationCDMKNNSensor data To reference this document use: http://resolver.tudelft.nl/uuid:8ed6b6ae-2633-43c3-a1ea-6d5e2089117f Part of collection Student theses Document type master thesis Rights © 2017 Mike de Waard Files PDF thesis.pdf 1.78 MB Close viewer /islandora/object/uuid:8ed6b6ae-2633-43c3-a1ea-6d5e2089117f/datastream/OBJ/view