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.This work has received funding from SESAR Joint Undertaking
(JU) within SIMBAD project under grant agreement No
894241. The JU receives support from the European Union’s
Horizon 2020 research and innovation programme and the
SESAR JU members other than the UnionPeer ReviewedPostprint (author's final draft
