A Machine-Learning Model for Forecasting Internal Wave Propagation in the Andaman Sea
A Machine-Learning Model for Forecasting Internal Wave Propagation in the Andaman Sea
复制标题
用于预测安达曼海内波传播的机器学习模型
DOI：
10.1109/jstars.2021.3063529
复制
发表时间：
2021-03
影响因子：
5.5
通讯作者：
Zheng Quanan
中科院分区：
文献类型：
--
作者：
Zhang Xudong;Li Xiaofeng;Zheng Quanan
Internal waves (IWs) are broadly distributed globally and have significant impacts on offshore engineering and underwater navigation. The prediction of IW propagation is a challenging task because of the complex factors involved. In this study, a machine-learning model was developed to predict IW propagation in the Andaman Sea. The model is based on a back-propagation neural network trained by 1189 IW samples, including the crest length and the peak-to-peak distance of IWs, extracted from 123 Moderate-Resolution Imaging Spectroradiometer (MODIS) images and 33 Ocean Land Color Instrument (OLCI) images acquired from 2015 to 2019 and corresponding ocean environment parameters. Using the leading wave crest within an IW packet as input, we ran the model to forecast the IW locations and compare them with satellite observations. The average root-mean-square difference between the model-forecasted and satellite-observed IW leading crest after one tidal cycle was 3.21 km. The corresponding averaged correlation coefficient was 0.95 and the average Fréchet Distance was 11.46 km. We reiterated the model run over two tidal periods and obtained similar statistical results, indicating the robustness of forecasting IW packets. We find that reducing the time step helped to improve forecasting accuracy. The influence of input errors and seasonal variations on model results are discussed and an analysis shows that the initial propagation direction introduced to the model is necessary for cross-propagating IW patterns. Comparisons with the Korteweg-de Vries equation results show that the developed model has better performance and is more robust.
登录
查看更多内容
比斯开湾陆架边缘内潮和孤立波的产生和传播
影响因子：
3.6
作者：
Xie X. H.;Cuypers Y.;Bouruet-Aubertot P.;Pichon A.;Lourenco A.;Ferron B.
通讯作者：
Ferron B.
雷达测高和MODIS图像高采样数据的内波观测
影响因子：
3.4
作者：
Xudong Zhang;Zhang Jie;Chenqing Fan;Jing Wang;Junmin Meng
通讯作者：
Junmin Meng
影响因子：
8.2
作者：
W. Alpers;Weigen Huang
通讯作者：
W. Alpers;Weigen Huang
影响因子：
13.5
作者：
I. Kozlov;D. Romanenkov;A. Zimin;B. Chapron
通讯作者：
I. Kozlov;D. Romanenkov;A. Zimin;B. Chapron
影响因子：
--
作者：
Wilamowski, Bogdan M.;Yu, Hao
通讯作者：
Yu, Hao