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1 May 2018 Analyzing the Characteristics of Ship-Generated Waves in an Inland-Restricted Channel Using an Artificial Neural Network (ANN)
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Abstract

Mao, L. and Chen, Y., 2018. Analyzing the characteristics of ship-generated waves in an inland-restricted channel using an artificial neural network (ANN).

Many empirical formulas based on experimental data have been proposed using regression analysis to analyze characteristics of ship-generated waves for engineering applications. However, ship-generated waves have an inherent variability, which suggests that a traditional method such as regression analysis might be inadequate. This paper presents an alternative nonlinear method using an artificial neural network (ANN) for clarifying the characteristics of ship-generated waves in an inland-restricted channel. A three-layered feed-forward back-propagation network with six input variables, five hidden layer nodes, and one output variable is established to predict the maximum ship-generated wave height based on sets of data collected from field observations conducted in Xicheng Canal. Comparisons of the predicted results from the proposed model with observed data show that the maximum ship-generated wave height can be predicted more accurately using the ANN compared to the two empirical formulas. The proposed model was then used to analyze the quantitative relationship between maximum ship-generated wave height in Xicheng Canal and various impact factors. Maximum ship-generated wave heights for 500-ton and 800-ton ships are given for engineering design in Xicheng Canal. These results show that the ANN model is a useful tool for both the prediction and mechanistic analysis of ship-generated waves in an inland-restricted channel.

©Coastal Education and Research Foundation, Inc. 2018
Lilei Mao and Yimei Chen "Analyzing the Characteristics of Ship-Generated Waves in an Inland-Restricted Channel Using an Artificial Neural Network (ANN)," Journal of Coastal Research 34(3), 618-627, (1 May 2018). https://doi.org/10.2112/JCOASTRES-D-17-00011.1
Received: 19 January 2017; Accepted: 17 April 2017; Published: 1 May 2018
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