Online Machine Learning for Adaptive Ballast Water Management

The paper proposes an innovative solution that employs online machine learning to continuously train and update models using sensor data from ships and ports. The proposed solution enhances the efficiency of ballast water management systems (BWMS), which are automated systems that utilize ultraviolet light and filters to purify and disinfect the ballast water that ships carry for maintaining their stability and balance. The solution allows it to grasp the complex and evolving patterns of ballast water quality and flow rate, as well as the diverse conditions of ships and ports. The solution also offers probabilistic forecasts that consider the uncertainty of future events that could impact the performance of ballast water management systems. An online machine learning architecture is proposed that can accommodate probabilistic based machine learning models and algorithms designed for specific training objectives and strategies. Three training methodologies are introduced: continuous training, scheduled training and threshold-triggered training. The effectiveness and reliability of the solution are demonstrated using actual data from ship and port performances. The results are visualized using time-based line charts and maps.