Artificial intelligence applied to floating boom behavior under waves and currents

Floating containment booms are an essential device in the fight against coastal pollution, allowing to contain the pollutant prior to its recovery. An Artificial Intelligence model is developed with the focus on the effective draft, or draft available for containment that a floating boom will provide in open waters. The dataset is obtained through an extensive laboratory campaign in which seven model booms are subjected to numerous wave and current combinations. This dataset is randomly divided into two subsets, one for training, the other for testing or validating the model. Input and output variables are selected based on dimensional analysis and laboratory results. The AI technique chosen for the model is multilayer feedforward artificial neural networks trained with the back-propagation algorithm, for their capability to apprehend higher-order patterns from the training examples and subsequently generalize them to other (validation) cases. In order to find an efficient network architecture, a comparative study involving 640 neural networks is carried out. Having selected the best performing architecture, the model is successfully validated; it is a virtual laboratory allowing to determine the effective draft that a certain boom design will provide under given wave and current conditions.