Using Artificial Neural Networks for Predicting Ship Fuel Consumption

Abstract In marine vessel operations, fuel costs are major operating which affect the overall profitability of maritime transport industry. The effective enhancement using ship will increase operation efficiency. Since consumption depends on different factors, such as weather, cruising condition, cargo load, and engine it is difficult to assess pattern for various types ships. Most traditional statistical methods do not consider these factors when predicting consumption. With technological development, models have been developed estimating patterns based data. Artificial Neural Networks (ANN) some most artificial modelling validating application ANN in improves accuracy regression analysing interactive relationships between factors. present review sheds light consolidating works carried out ANN, with an emphasis topics structure, prediction algorithms. Future research directions also proposed can be a benchmark mathematical ANN.