基于船舶自动识别系统与人工神经网络的船舶载重预测

Prediction of Vessel Load Based on Vessel Automatic Identification System and Artificial Neural Network

传统的船舶载重获取方法多基于人工查表、经验计算和回归分析,这些方法操作麻烦,自动化水平较低,计算过程充斥着大量经验数值和统计公式,而一些统计公式和经验数值随着船型的变化已经过时,需要及时更新。目前,获取全球船舶动态载重是一项困难的工作。文中提出基于船舶自动识别系统和人工神经网络的船舶载重预测方法,该方法分析了船舶长度、宽度、吃水深度、船舶类型与船舶载重的数学关系,建立了Adam-Dropout优化的多层人工神经网络,确定了船舶载重预测的最佳输入组合;同时,还探究了该方法适用的船舶类型。实验结果表明,ANN的输入为船舶长度、宽度、吃水深度、船舶类型时,预测效果最好,MAPE误差为7.63%,最小APE误差可达0.05%;神经网络的隐含层数为4、神经元个数为11时,预测结果最优;该方法适用于原油船、散货船、化学品船、集装箱船、液化天然气船、液化石油气船、成品油船、杂货船、冷冻船,预测MAPE误差均在15%以内。

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may also have a low level of automation.On the other way,the calculation process is full of a large number of outdated empirical values and statistical formulas which sometimes need to be updated timely with the changes of vessel type.At present,it is a difficult task to obtain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%.