Next Article in Journal
Locally Oriented Scene Complexity Analysis Real-Time Ocean Ship Detection from Optical Remote Sensing Images
Next Article in Special Issue
Smart Ocean: A New Fast Deconvolved Beamforming Algorithm for Multibeam Sonar
Previous Article in Journal
Enhanced Sensitivity of a Hydrogen Sulfide Sensor Based on Surface Acoustic Waves at Room Temperature
Previous Article in Special Issue
A Combined Ray Tracing Method for Improving the Precision of the USBL Positioning System in Smart Ocean
Article Menu
Issue 11 (November) cover image

Export Article

Open AccessArticle
Sensors 2018, 18(11), 3797; https://doi.org/10.3390/s18113797

TD-LSTM: Temporal Dependence-Based LSTM Networks for Marine Temperature Prediction

1
College of Computer Science and Technology, Jilin University, Changchun 130012, China
2
State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
3
Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province, School of Software, Dalian University of Technology, Dalian 116024, China
*
Author to whom correspondence should be addressed.
Received: 10 October 2018 / Revised: 27 October 2018 / Accepted: 2 November 2018 / Published: 6 November 2018
(This article belongs to the Special Issue Smart Ocean: Emerging Research Advances, Prospects and Challenges)
Full-Text   |   PDF [2335 KB, uploaded 6 November 2018]   |  

Abstract

Changes in ocean temperature over time have important implications for marine ecosystems and global climate change. Marine temperature changes with time and has the features of closeness, period, and trend. This paper analyzes the temporal dependence of marine temperature variation at multiple depths and proposes a new ocean-temperature time-series prediction method based on the temporal dependence parameter matrix fusion of historical observation data. The Temporal Dependence-Based Long Short-Term Memory (LSTM) Networks for Marine Temperature Prediction (TD-LSTM) proves better than other methods while predicting sea-surface temperature (SST) by using Argo data. The performances were good at various depths and different regions. View Full-Text
Keywords: long short-term memory (LSTM); temporal dependence; sea surface temperature (SST); prediction long short-term memory (LSTM); temporal dependence; sea surface temperature (SST); prediction
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Liu, J.; Zhang, T.; Han, G.; Gou, Y. TD-LSTM: Temporal Dependence-Based LSTM Networks for Marine Temperature Prediction. Sensors 2018, 18, 3797.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top