MATEC Web Conf.
Volume 296, 20192019 7th International Conference on Traffic and Logistic Engineering (ICTLE 2019)
|Number of page(s)||5|
|Published online||22 October 2019|
Prediction of Ship Traffic Volume in Jiujiang Port Based on Genetic Wavelet Neural Network
1 College of Wuhan University of Technology, Hubei Provincial Key Laboratory of Inland navigation technique, china
2 College of Wuhan University of Technology, Hubei Provincial Key Laboratory of Inland navigation technique, china
a Corresponding author: firstname.lastname@example.org
In recent years, the traffic volume of the Yangtze River has increased dramatically. In order to provide more favorable assistance to port planning and traffic management, the accuracy of port ship traffic volume prediction is very important. In this paper, genetic algorithm and wavelet analysis and neural network are used to construct the genetic wavelet neural network model prediction model, and BP neural network prediction model is established. The ship volume of Jiujiang Port is used as experimental data to simulate and analyze. The results show that the prediction accuracy of the genetic wavelet neural network prediction model is significantly higher than that of the BP neural network prediction model. It is proved that the genetic wavelet neural network has broad application prospects for ship traffic flow prediction in the Yangtze River port. This method has practical application significance.
© The Authors, published by EDP Sciences, 2019
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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