DeepOcean: A general deep learning framework for spatio-temporal ocean sensing data prediction
Document Type
Article
Publication Date
1-1-2020
Department
Department of Electrical and Computer Engineering
Abstract
The emerging Internet of Underwater Things (IoUT) and deep learning technologies are combined to provide a novel, intelligent, and efficient data processing and analyzing schema, which facilitates the sensing and computing abilities for the smart ocean. The underwater acoustic (UWA) communication network is an essential part of IoUT. The thermocline, in which temperature and density change drastically, affects the connectivity and communication performance between IoUT nodes, as well as the network topologies. In this paper, we propose DeepOcean, a deep learning framework for spatio-temporal ocean sensing data prediction, which consists of a generative module and a prediction module. We implement the generative module with a multi-layer perceptron (MLP) to capture the spatial dependencies and construct high-resolution data based on sparse observations. The prediction module is implemented with our proposed Multivariate Convolutional LSTM (MVC-LSTM) neural network, which captures both the spatio-temporal dependencies and the interactions of different oceanographic features for prediction. We evaluate the effectiveness of DeepOcean with Argo data, where the proposed framework outperforms fifteen state-of-art baselines in terms of accuracy.
Publication Title
IEEE Access
Recommended Citation
Gou, Y.,
Zhang, T.,
Liu, J.,
Wei, L.,
&
Cui, J.
(2020).
DeepOcean: A general deep learning framework for spatio-temporal ocean sensing data prediction.
IEEE Access,
8, 79192-79202.
http://doi.org/10.1109/ACCESS.2020.2990939
Retrieved from: https://digitalcommons.mtu.edu/michigantech-p/2759
Publisher's Statement
© 2013 IEEE. Publisher’s version of record: https://doi.org/10.1109/ACCESS.2020.2990939