Towards Cleaner Ports: Predictive Modeling of Sulfur Dioxide Shipping Emissions in Maritime Facilities Using Machine Learning

Abstract

Maritime ports play a pivotal role in fostering the growth of domestic and international trade and economies. As ports continue to expand in size and capacity, the impact of their operations on air quality and climate change becomes increasingly significant. While nearby regions may experience economic benefits, there are significant concerns regarding the emission of atmospheric pollutants, which have adverse effects on both human health and climate change. Predictive modeling of port emissions can serve as a valuable tool in identifying areas of concern, evaluating the effectiveness of emission reduction strategies, and promoting sustainable development within ports. The primary objective of this research is to utilize machine learning frameworks to estimate the emissions of SO2 from ships during various port activities, including hoteling, maneuvering, and cruising. By employing these models, we aim to gain insights into the emission patterns and explore strategies to mitigate their impact. Through our analysis, we have identified the most effective models for estimating SO2 emissions. The AutoML TPOT framework emerges as the top-performing model, followed by Non-Linear Regression with interaction effects. On the other hand, Linear Regression exhibited the lowest performance among the models evaluated. By employing these advanced machine learning techniques, we aim to contribute to the body of knowledge surrounding port emissions and foster sustainable practices within the maritime industry.