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Abstract

Department of Technology and Innovation ManagementMany machine learning applications are being employed to forecast weather conditions. In this paper, we focus more on small-scale weather forecasts with limited meteorological observation data. When oil refinery companies in non-oil-producing countries import crude oil by VLCCs (Very Large Crude Carriers), VLCCs unload crude oil to onshore storage tanks using SPM (Single Point Buoy Mooring System). Weather conditions in the offshore area where loading buoys are anchored are critical in determining whether unloading process is possible. The current practice of such decision making relies mostly on human experiences, and the predictive accuracy of the current practice is reported as about 75%. We tested machine learning methods to see if these methods can increase predictive accuracy in this problem of classification, the possibility of unloading given weather conditions such as wave heights, wind speeds, and wind directions. The results of our analysis showed that random forest and XGBoost have much better performance (more than 90%) than support vector machines and logistic regression in predicting unloading conditions in the time range from one hour to three days.clos