Journal
SENSORS
Volume 18, Issue 11, Pages -Publisher
MDPI
DOI: 10.3390/s18113797
Keywords
long short-term memory (LSTM) temporal dependence; sea surface temperature (SST); prediction
Funding
- National Natural Science Foundation of China [61631008, 61572172, 61872124]
- Fundamental Research Funds for the Central Universities [2017TD-18, DUT17RC(3)094]
- State Key Laboratory of Robotics [2015-O06]
- National Natural Science Foundation of China-Guangdong Joint Fund [U180120020]
- program for Liaoning Excellent Talents in University [LR2017009]
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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.
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