关键词: 吃水深度, 船舶载重, 船舶自动识别系统, 平均绝对百分误差, 人工神经网络

Abstract: Traditional acquisition methods of vessel load are mostly based on manual observation,empirical calculation and regression analysis.These methods are usually difficult to operate,which may also have a low level of automation.On the other way,the calculation process is full of a large number of outdated empirical values and statistical formulas which sometimes need to be updated timely with the changes of vessel type.At present,it is a difficult task to obtain vessel’s dynamic load all around the world.This paper presented a prediction method of vessel load based on vessel automatic identification system and artificial neural network,analyzed the mathematical relationship between vessel’s length,breadth,draught,vessel types and vessel load,established a multi-layer artificial neural network with Adam-Dropout optimization,found out the best input types of artificial neural network and its suitable vessel types.Experiments show that the prediction result is the best when the inputs of ANN are length,breadth,draught and vessel type,the MAPE values of ANN can reach to 7.63% while the minimum APE value reaches to 0.05%.The prediction result is the best when the number of hidden layers is 4 and the number of neurons is 11.The method is suitable for crude oil tankers,bulk carriers,chemical tankers,container vessels,liquefied natural gas tankers,liquefied petroleum gas tankers,oil products tankers,grocery cargos and refrigeration vessels.The MAPE values of them are all less than 15%.

Key words: Artificial neural network, Draught, Mean absolute percentage error, Vessel automatic identification system, Vessel load