Title: Risk models based on uncertainty quantification for illicit traffic time series in customs context

Authors: Lamia Hammadi; Eduardo Souza De Cursi; Vlad Stefan Barbu; Abdellah Ait Ouahman

Addresses: Laboratory of Engineering Sciences for Energy LabSIPE, National School of Applied Sciences of El Jadida, UCD, Morocco; Laboratory of Mechanics of Normandy LMN, National Institute of Applied Sciences, INSA of Rouen – Normandy, France ' Laboratory of Mechanics of Normandy LMN, National Institute of Applied Sciences, INSA of Rouen – Normandy, France ' Laboratory of Mathematics Raphaël Salem, Faculty of Mathematics, University of Rouen – Normandy, UMR 6085, France ' Engineering School, Private University of Marrakech, Morocco

Abstract: Due to the increase of the worldwide trade, the flow of goods crossing borders is increasing; at the same time, the threats faced by society are growing. Customs are encountering many challenges and looking for new tools of decision making for countering such risks. This paper develops models based on uncertainty quantification to analyse the behaviour of the risk time series in customs. We start by introducing the Hilbertian approach related to the representation of random variables and addressing these approximations and their applications in UQ. Then we discuss an extension where these models are applied to handle the seasonal components of risks. The models are fitted to the seized quantities of the illicit traffic on five sites using moment matching method. The results provide a good description of important properties of the data and a tool of decisions making on risk analysis in cases of threats in global supply chain.

Keywords: uncertainty quantification; Hilbert expansion; risk time series; moment matching method; customs; cumulative distribution function; CDF.

DOI: 10.1504/IJSTL.2022.120672

International Journal of Shipping and Transport Logistics, 2022 Vol.14 No.1/2, pp.3 - 32

Received: 03 Mar 2020
Accepted: 05 Dec 2020
Published online: 02 Feb 2022 *

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