RIPTIDE: Learning violation prediction models from boarding activity data

Part of the U.S. Coast Guard´s mission is to monitor vessels and their operators for compliance with a large body of safety and fisheries regulations. Recently the Coast Guard has devised a system called OPTIDE, which aims at improving operations efficiency by ranking vessels via a risk score computed from current information and aggregated past boarding observations. Ships with higher risk should be preferentially boarded, since they have higher probability of being in violation of some regulation. To improve upon OPTIDE, we developed RIPTIDE which uses machine learning to automatically learn a more fine-grained and data-driven violation prediction and ranking model from past boarding activity data. The learning problem is challenging, since the data is very unbalanced (only about 20% of all boardings actually find some violation), it has significant sampling bias, and in general the signal for predicting violations is weak. Nevertheless, our best RIPTIDE model outperforms OPTIDE by up to 86% on a ranking experiment. The main reason for this improvement comes from being able to distinguish vessels in a more fine-grained manner, which allows RIPTIDE to make winning decisions more often, even if the underlying signal is very weak. A software package implementing RIPTIDE has been developed to allow the Coast Guard to experiment with the learned models and apply them to operational data.