Motion simulation of moorings using optimized LSTM neural network  

作　　者：Zhiyuan ZHUANG Fangjie YU Ge CHEN 

机构地区：[1]Department of Marine Technology,College of Information Science and Engineering,Ocean University of China,Qingdao 266100,China [2]Laoshan Laboratory,Qingdao 266237,China

出　　处：《Journal of Oceanology and Limnology》2023年第5期1678-1693,共16页海洋湖沼学报（英文）

基　　金：Supported by the Laoshan Laboratory (Nos.LSKJ202201302-5,LSKJ202201405-1,LSKJ202204304)。

摘　　要：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.

关 键 词：MOORING motion simulation long short-term memory(LSTM) optimization strategy hybrid deep learning 

分 类 号：P715[天文地球—海洋科学]