Forêts aléatoires et sélection de variables : analyse des données des enregistreurs de vol pour la sécurité aérienne

摘要

New recommendations require airlines to establish a safety management strategy to keep reducing the number of accidents. The flight data recorders have to be systematically analysed in order to identify, measure and monitor the risk evolution. The aim of this thesis is to propose methodological tools to answer the issue of flight data analysis. Our work revolves around two statistical topics: variable selection in supervised learning and functional data analysis. The random forests are used as they implement importance measures which can be embedded in selection procedures. First, we study the permutation importance measure when the variables are correlated. This criterion is extended for groups of variables and a new selection algorithm for functional variables is introduced. These methods are applied to the risks of long landing and hard landing which are two important questions for airlines. Finally, we present the integration of the proposed methods in the software FlightScanner implemented by Safety Line. This new solution in the air transport helps safety managers to monitor the risks and identify the contributed factors.