A novel machine-learning based prediction model for ship manoeuvring emissions by using bridge simulator

Abstract

Emissions from ships in ports have primary adverse impacts on human health and the regional environment, as ports are close to residential areas. This study investigates the emissions originated from own ship and tugs assisting the manoeuvres. More than 300,000 row main engine rpm data of the own ship and tugboats used in the berthing manoeuvre to a fictitious port executed by 92 actual maritime pilots in a full mission bridge simulator were exported from the system. Based on the rpm data, emission values were calculated by bottom-up methodologies where resulting emissions are differ up to 1.85 times. A significant correlation between the emission results and pilots’ demographics and experience-based backgrounds are observed. By utilising MATLAB Machine Learning Toolbox, a manoeuvring emission footprint prediction model is developed for the pilots with a consistency of 73%. The proposed model provides a solution that can support strategic planning in determining emission footprints and developing mitigation measures in a cost-effective manner.