Proceedings of the Thirty-second (2022) International Ocean and Polar Engineering Conference
Shanghai, China, June 5–10, 2022
Copyright © 2022 by the International Society of Offshore and Polar Engineers (ISOPE)
ISBN 978-1-880653-81-4; ISSN 1098-6189

www.isope.org

A machine learning ship’s speed prediction model and sailing time control strategy
Xiao Lang1, Da Wu2*, Wengang Mao1
1Dept. of Mechanics and Maritime Sciences, Chalmers University of Technology.

Gothenburg, Västra Götaland, Sweden
National Engineering Research Center for Water Transport Safety, Wuhan University of Technology.
Wuhan, Hubei, China2

propeller racing, and extreme ship motions by intentionally adjustingship engine power or RPM. The added resistance causes the involuntary
speed loss due to wind, waves, and propulsion efficiency loss in harshweather conditions (Chuang and Steen, 2013). The prediction of a ship’s
speed requires comprehensive knowledge about resistance, propulsion,machinery, and automatic control. Since modern ships’ complexity andexposure to external factors such as wind, waves, and currents, it is noteasy to accurately estimate ship actual sailing speed. And there is alwaysa discrepancy between the theoretical calculation and real measurements.
Sometimes this difference can be very significant.

ABSTRACTThis paper proposes a machine learning based ship speed over ground
prediction model, driven by the eXtreme Gradient Boosting (XGBoost)algorithm. The dataset is acquired from a world-sailing chemical tanker
with five years of full-scale measurements. The model is trained using
encountered metocean environments and ship operation profiles in two
scenarios, i.e., through RPM or propulsion power. This model is further
combined with the particle swarm optimization (PSO) algorithm tointegrate a sailing time control method. It optimizes constant RPM orpower operation strategy to meet the requirements of fixed ETA.

With increasing interest in ship energy efficiency assessment andemission control, it is becoming more and more common to equip ships
with sensors and data acquisition systems for in-service monitoring(Dalheim and Steen, 2020). It is an opportunity to extract valuable
information from ships in operation, which raises understanding of theship’s actual performance and sailing speed. Machine learning aims toactualize data-driven models by substituting traditional analysis models
based on physical principles. Sophisticated machine learning methods
have been researched to develop more accurate data-driven ship speed
prediction models (Valcic et al., 2011; Mao et al., 2016; Brandsaeter andVanem, 2018; Coraddu et al., 2019; Abebe et al., 2020; Milakovic et.al,
2020; Moreira et al., 2021; Yuan et al., 2021). Due to a lack of access toship data, relatively low-resolution data such as noon reports, andnavigation simulator data are frequently employed for model training.
Those ship speed prediction models are commonly trained by fuelconsumption, shaft thrust, auxiliary power, draft, or the ship’s 6 DOF
motions. To use data-driven ship speed prediction models in realnavigation, it is necessary to model a ship’s speed in terms of
encountered weather conditions.

KEY WORDS: Machine learning; speed over ground; ETA; full-scalemeasurements; XGBoost; particle swarm optimization.

INTRODUCTION
The uncertainty over estimated time of arrival (ETA), i.e., the expectedarrival date and time of a shipment to a planned destination, is one of themain problems in the decision-making stage within the entire maritime
transport chain (Valcic et al., 2011, Wang et al., 2021). This uncertainty
reduces the reliability of the schedule, resulting in increased delays anddecreased productivity for inland transportation (Rob et al., 2015). The
ship arrival delays add to the cost of vessel operating and supply chain
management, and efficient logistic plan cannot be formulated(Vernimmen et al., 2007). Thus, it is important to accurately predict therequired sailing time, and the ship speed over ground (SOG) is the mostessential factor in determining the ETA (Wang et al., 2020). The
inevitable ship speed decrease due to various weather conditions always
leads ship operators to frequently revise ETA 24 hours before arrival(Fancello et al., 2011). A reliable ship speed prediction is becoming moreessential in improving marine traffic control, fleet management, cargohandling operations (Prpic-Orsic et al., 2016).

This paper applies the eXtreme Gradient Boosting (XGBoost) algorithm
to establish a machine learning model to predict ship SOG, concerning
the encountered wave, wind, and current conditions. The model isestablished under two scenarios, i.e., based on RPM or propulsion power,
since onboard crews always adjust these two settings to operate thevessel at sea. The ship operation profiles dataset was collected from a

A ship’s speed reduction can be divided into voluntary or involuntary(Faltinsen et al., 1980). The voluntary speed reduction avoids slamming,3598