Prediction of shear stress induced by shoaling internal solitary waves based on machine learning method

Journal article


Tian, Zhuangcai, Liu, Hanlu, Zhang, Shaotong, Wu, Jinran and Tian, Jiahao. (2023). Prediction of shear stress induced by shoaling internal solitary waves based on machine learning method. Marine Georesources and Geotechnology. 41(2), pp. 221-232. https://doi.org/10.1080/1064119X.2022.2136045
AuthorsTian, Zhuangcai, Liu, Hanlu, Zhang, Shaotong, Wu, Jinran and Tian, Jiahao
Abstract

Recently, the interactions between internal solitary waves (ISWs) and the seabed have directed increasing attention to ocean engineering and offshore energy. In particular, ISWs induce bottom currents and pressure fluctuations in deep water. In this paper, we propose a method for predicting the shear stress induced by shoaling ISWs based on machine learning, and the developed approach can be used to quickly determine the safety and stability of ocean engineering. First, we provided a basic dataset for model training. Four machine learning models were selected to predict the shear stress induced by shoaling ISWs under different trim conditions. The results indicated that the performance of the convolutional neural network-long short-term memory (CNN-LSTM) forest prediction model was significantly better than the three other tested models, including long short-term memory (LSTM), support vector regression (SVR) and deep neural network (DNN) models. Therefore, the CNN-LSTM forest prediction model was the optimal model for predicting the shear stress induced by shoaling ISWs. Specifically, each metric of the CNN-LSTM model was smaller than that of the other three, and the root mean squared error to the standard deviation ratio was closest to 0.7. In addition, the CNN-LSTM model significantly outperformed the SVR and DNN models in terms of the length of prediction time. The predicted values by the CNN-LSTM model were consistent with the experimental values. The method for predicting shear stress based on machine learning in this paper can be used to predict the shear stress induced by shoaling ISWs, guide future field experiment designs, reduce damage to the seabed caused by ISWs, and promote the development of ocean engineering in deep water.

Keywordsshear stress; prediction; internal solitary waves; machine learning; sediment
Year2023
JournalMarine Georesources and Geotechnology
Journal citation41 (2), pp. 221-232
PublisherTaylor & Francis
ISSN1064-119X
Digital Object Identifier (DOI)https://doi.org/10.1080/1064119X.2022.2136045
Scopus EID2-s2.0-85141087490
Publisher's version
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All rights reserved
File Access Level
Controlled
Output statusPublished
Publication dates
Online21 Oct 2022
Publication process dates
Accepted12 Sep 2022
Deposited18 Jul 2023
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