Enhancing shipboard oil pollution prevention: Machine learning innovations in oil discharge monitoring equipment

Abstract

Maritime operations face significant challenges in environmental stewardship, particularly in managing oil discharges from tankers as mandated by the International Convention for the Prevention of Pollution from Ships (MARPOL) Annex I, Regulation 34. Traditional Oil Discharge Monitoring Equipment (ODME) methods rely on manual decision-making, often failing to accurately identify MARPOL-defined no-go zones, estimate operation completion times, and recommend course alterations during decanting operations. This study introduces a novel approach by integrating advanced machine learning techniques-Extreme Gradient Boosting (XGBoost) and Light Gradient Boosting Machine (LightGBM)-to enhance ODME operations. Specifically, these models automate the identification of no-go zones and optimize operational decisions, leading to a 99 % accuracy rate in compliance with MARPOL regulations and an operational time estimation error margin of <1 %. Unlike traditional methods, our approach leverages large datasets and real-time GPS (Global Positioning System) data, significantly reducing human error and enhancing both environmental compliance and operational efficiency. To our knowledge, this is the first study to specifically address the application of machine learning to decanting operations under MARPOL Annex I, marking a significant advancement in maritime environmental management.

Keywords: Extreme gradient boosting; Light gradient boosting machine; MARPOL convention; Machine learning; Oil discharge monitoring; Shipboard ocean pollution prevention.