@inproceedings{cc2fdfd13d9f4a3b828e491956c67ec1,
title = "Convolutional LSTM networks for seawater temperature prediction",
abstract = "The seawater temperature has the characteristics of both spatial and temporal. To better predict seawater temperature, we propose a convolutional long short-term memory recurrent neural network method based on thermohaline data. We use convolutional neural networks to extract features from the thermohaline data in near time domains to obtain corresponding values of these features. These features are input into the LSTM recurrent neural network for the temporal prediction of seawater temperature. The method was empirically evaluated and compared to the RNN methods considering only a single variable and the methods considering temperature and salinity. The results show that the Thermohaline Convolutional LSTM Model has significant advantages over other methods and can predict changes in seawater temperature more accurate at different depths.",
keywords = "Convolutional Neural Network, Long short-term memory, seawater temperature prediction, ther-mohaline data, time series",
author = "Jun Liu and Tong Zhang and Yu Gou and Xiaoyu Wang and Bo Li and Wenxue Guan",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2019 ; Conference date: 11-12-2019 Through 13-12-2019",
year = "2019",
month = dec,
doi = "10.1109/ICSIDP47821.2019.9173301",
language = "英语",
series = "ICSIDP 2019 - IEEE International Conference on Signal, Information and Data Processing 2019",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "ICSIDP 2019 - IEEE International Conference on Signal, Information and Data Processing 2019",
address = "美国",
}