To respond to the compelling air pollution programs, shipping companies are nowadays setting‐up on their fleets modern multisensor systems that stream massive amounts of observational data, which can be considered as varying over a continuous domain. Motivated by this context, a novel procedure is proposed, which extends classical multivariate techniques to the monitoring of multivariate functional data and a scalar quality characteristic related to them. The proposed procedure is shown to be also applicable in real time and is illustrated by means of a real‐case study in the maritime field on the continuous monitoring of operating conditions (ie, the multivariate functional data) and total CO2 emissions (ie, the scalar quality characteristic) at each voyage of a cruise ship. The real‐time monitoring is particularly helpful for promptly supporting managerial decision making by indicating if and when an anomaly occurs during the navigation.
The new EU Regulation urges shipping operators to set up systems for the monitoring, reporting, and verification of CO2 emissions. Indeed, new monitoring data acquisition systems installed on modern ships have brought a navigation data overload that needs to be correctly handled to make proper decisions about their operation. However, in today's market, there is no standard solution or method available that can be robustly adopted in real environments for the shipping industry. In view of the novel attempts for solving this issue proposed by statisticians, marine engineers, and practitioners, this paper presents an extensive comparison of several regression techniques that can exploit the navigation information usually available in modern ships: variable selection methods, penalized regression methods, latent variable methods and tree‐based ensemble methods. The comparison is made by means of operational data collected on a Ro‐Pax cruise ship owned by the Italian shipping company Grimaldi Group. The goal of this analysis is twofold: (1) to identify methodologies with more potential at analyzing the data collected from this shipping industry scenario and (2) to develop a predictive model for CO2 emissions with good characteristics of accuracy and robustness.
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