Application of Artificial Neural Network (ANN) for Prediction of Maritime Safety

In the field of marine, prediction of the marine safety is very important. It is reported that defects exist in the small intestinal epithelial barrier of inflammatory bowel disease, which might be associated with increased intestinal permeability at a very early stage. All endangered large whale species are vulnerable to collisions with large ships; and “ship strikes” are the greatest known threat to one of the world’s rarest whales, the North Atlantic right whale. The magnitude of this threat is likely to increase as maritime commerce expands. Factors influencing the incidence and severity of ship strikes are not well understood, although vessel speed appears to be a strong contributor. The purpose of this study was to characterize the hydrodynamic effects near a moving hull that may cause a whale to be drawn to or repelled from the hull, and to assess the accelerations exerted on a whale at the time of impact. Using scale models of a container ship and a right whale in experimental flow tanks, we assessed hydrodynamic effects and measured accelerations experienced by the whale model in the presence of a moving vessel. Using an artificial neural network (ANN) prediction of the marine safety, results are compared with experimental values and deviations are determined. Based on the implemented investigations, minimal deviations between experimental and predicted values are obtained and can be concluded that ANN can be used for prediction of the marine safety.