Ensemble Forecasting For Predicting Petroleum Products Traffic in Oil Terminal

The precise prediction of petroleum product traffic holds vital significance for optimizing maritime operations and allocating resources efficiently within the industry. This research evaluates the performance of three distinct machine learning models, namely Extra Trees, K-Nearest Neighbors, and Decision Tree, and introduces an ensemble forecasting methodology to augment the accuracy of predicting traffic volume for petroleum products at an oil terminal. The investigation employs a two-year dataset derived from oil and gas tankers at Mohammedia port for analysis. Empirical findings reveal that the ensemble learner model surpasses the individual models in predicting traffic volume for petroleum products, demonstrating enhanced accuracy across both test and validation datasets.