Motion simulation of moorings using optimized LSTM neural network

Zhiyuan ZHUANG; Fangjie YU; Ge CHEN

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Motion simulation of moorings using optimized LSTM neural network

Physics | Updated：2024-10-14

- Motion simulation of moorings using optimized LSTM neural network
- Motion simulation of moorings using optimized LSTM neural network
- 海洋湖沼学报(英文) 2023年41卷第5期页码：1678-1693
- Affiliations：
  
  1.Department of Marine Technology, College of Information Science and Engineering, Ocean University of China, Qingdao 266100, China
  2.Laoshan Laboratory, Qingdao 266237, China
- Author bio：
  
  yufangjie@ouc.edu.cn
- Funds：
  
  the Laoshan Laboratory(LSKJ202201302-5;LSKJ202201405-1;LSKJ202204304)
- DOI：
  中图分类号：
- 收稿：2022-04-27，
  
  录用：2022-06-08，
  
  纸质出版：2023-09-01
- Accepted：
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Motion simulation of moorings using optimized LSTM neural network[J]. 海洋湖沼学报(英文), 2023,41(5):1678-1693.

ZHUANG Zhiyuan,YU Fangjie,CHEN Ge.Motion simulation of moorings using optimized LSTM neural network[J].Journal of Oceanology and Limnology,2023,41(05):1678-1693.
Motion simulation of moorings using optimized LSTM neural network[J]. 海洋湖沼学报(英文), 2023,41(5):1678-1693. DOI：

ZHUANG Zhiyuan,YU Fangjie,CHEN Ge.Motion simulation of moorings using optimized LSTM neural network[J].Journal of Oceanology and Limnology,2023,41(05):1678-1693. DOI：

摘要

Abstract

Mooring arrays have been widely deployed in sustained ocean observation in high resolution to measure finer dynamic features of marine phenomena. However

the irregular posture changes and nonlinear response of moorings under the effect of ocean currents face huge challenges for the deployment of mooring arrays

which may cause the deviations of measurements and yield a vacuum of observation in the upper ocean. We developed a data-driven mooring simulation model based on LSTM (long short-term memory) neural network

coupling the ocean current with position data from moorings to predict the motion of moorings

including single-step output prediction and multi-step prediction. Based on the predictive information

the formation of the mooring array can be adjusted to improve the accuracy and integrity of measurements. Moreover

we proposed the cuckoo search (CS) optimization algorithm to tune the parameters of LSTM

which improves the robustness and generalization of the model. We utilize the datasets observed from moorings anchored in the Kuroshio Extension region to train and validate the simulation model. The experimental results demonstrate that the model can remarkably improve prediction accuracy and yield stable performance. Moreover

compared with other optimization algorithms

CS is more efficient and performs better in simulating the motion of moorings.

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Keywords

references

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