基于深度网络和船舶交通流的航道水深预测方法研究  

Research on Waterway Depth Prediction Method Based on Depth Network and Ship Traffic Flow

作　　者：杨帆[1] 何正伟[1,2,3] 何帆 YANG Fan;HE Zhengwei;HE Fan(School of Navigation,Wuhan University of Technology,Wuhan 430063,China;Hubei Inland Shipping Technology Key Laboratory,Wuhan 430063,China;National Engineering Research Center for Water Transport Safety,Wuhan 430063,China)

机构地区：[1]武汉理工大学航运学院,武汉430063 [2]内河航运技术湖北省重点实验室,武汉430063 [3]国家水运安全工程技术研究中心,武汉430063

出　　处：《武汉理工大学学报（交通科学与工程版）》2019年第1期130-135,共6页Journal of Wuhan University of Technology（Transportation Science & Engineering）

基　　金：中央高校基本科研业务费专项资金资助(2018-zy-127)

摘　　要：根据水上交通的特点,提出了一种基于船舶自动识别系统(AIS)大数据,构建深度网络模型预测航道水深的方法,并利用最新航道水深数据作为标签验证.分别利用深度神经网络算法和决策树-深度神经网络结合的DT-NN算法,对水深数据和AIS数据进行学习.实验结果表明,深度神经网络算法的预测准确度为90.84%,DT-NN算法的预测准确度为91.15%,因此,采用决策树和深度神经网络结合的DT-NN算法对于水深预测的模型准确率较高,对于弥补航道水深数据的不足,指导船舶安全航行.According to the characteristics of water transportation,this paper proposed a method to build a depth network model to predict channel water depth based on ship automatic identification system(AIS)big data,and used the latest channel water depth data as label verification.The depth neural network algorithm and DT-NN algorithm combined with decision tree and depth neural network were used to learn water depth data and AIS data respectively.The experimental results show that the prediction accuracy of the deep neural network algorithm is 90.84%,and the prediction accuracy of DT-NN algorithm is 91.15%.Therefore,the DT-NN algorithm using the combination of decision tree and deep neural network has higher accuracy for water depth prediction,and plays an important role in making up for the shortage of channel water depth data and guiding the safe navigation of ships.

关 键 词：AIS大数据 航道水深 深度神经网络 决策树 数据挖掘 

分 类 号：U612.26[交通运输工程—船舶及航道工程]