Data-Driven Approach for Ship Emissions Prediction: A Case Study on the Saint Lawrence River

The maritime sector plays an important role in global greenhouse gas (GHG) emissions, which presents a major challenge for climate mitigation efforts. As regulatory frameworks and environmental goals become more stringent, accurately predicting emissions in this sector is crucial for informed decision-making and effective policy implementation. This article presents a data-driven approach to predicting maritime emissions using advanced machine learning techniques. Our proposed work predicts emissions from maritime activities, integrating various data sources to improve accuracy and reliability. The aim is to provide actionable insights to monitor ship emissions and assess their environmental impact.