Predictive Modeling Performance Comparison of Port-Based Hydrocarbon Emissions Using Multiple Linear Regression, Decision Trees and Random Forest

Ports are primarily used for maritime activities such as cruising, maneuvering, and hoteling, which facilitate imports and exports. These activities are crucial for economic growth and development. However, when port activities are scaled up, they become hazardous to the environment and public health due to significant emissions. This exacerbates existing climate change and air quality issues. Despite the economic benefits, it is essential to consider the environmental impacts and strive to mitigate or reduce them to prevent harmful effects. To address this problem, machine learning tools and techniques can be employed to predict emissions and identify the predictors that influence them. The primary goal of this study is to forecast hydrocarbon (HC) emissions resulting from activities like cruising, maneuvering, and hoteling using machine learning models. By implementing these models, we can uncover emission patterns and develop strategies to reduce their impact. Among the models tested, the Random Forest model demonstrated the best performance, followed by Decision Trees. In contrast, our implementation of multiple linear regression models indicated overfitting of the data, making them less suitable for our purposes.