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Data-driven estimation of flights’ hidden parameters

Paper ID

SIDs-2022-054

Conference

SESAR Innovation Days

Year

2022

Theme

Machine Learning II

Project Name

SESAR 2020 ER4 project SIMBAD

Keywords:

AI/ML, data-driven estimation, Hidden parameters, KPIs, prediction

Authors

George Vouros, Theodore Tranos, Konstantinos Blekas, Georgios Santipantakis, Marc Melgosa and Xavier Prats

DOI

Project Number

894241

Abstract

This paper presents a data-driven methodology for the estimation of flights’ hidden parameters, combining mechanistic and AI/ML models. In the context of this methodology the paper studies several AI/ML methods and reports on evaluation results for estimating hidden parameters, in terms of mean absolute error. In addition to the estimation of hidden parameters themselves, this paper examines how these estimations affect the prediction of KPIs regarding the efficiency of flights using a mechanistic model. Results show the accuracy of the proposed methods and the benefits of the proposed methodology. Indeed, the results show significant advances of data-driven methods to estimate hidden parameters towards predicting KPIs.