# Prediction of Sea Surface Temperature using Long Short-Term Memory

arXiv: https://arxiv.org/abs/1705.06861 · HTML: https://mlsanity.com/paper/1705.06861/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 May 2017.

## Built on by (most cited first, 9 of 9)

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- [Physical Knowledge Enhanced Deep Neural Network for Sea Surface Temperature Prediction](https://mlsanity.com/paper/2304.09376/) (2023)
- [Physics-Guided Generative Adversarial Networks for Sea Subsurface Temperature Prediction](https://mlsanity.com/paper/2111.03064/) (2021)
- [Spatial-Temporal Data Mining for Ocean Science: Data, Methodologies, and Opportunities](https://mlsanity.com/paper/2307.10803/) (2023)
- [Deep learning based closed-loop optimization of geothermal reservoir production](https://mlsanity.com/paper/2204.08987/) (2022)
- [STFM: A Spatio-Temporal Information Fusion Model Based on Phase Space Reconstruction for Sea Surface Temperature Prediction](https://mlsanity.com/paper/2504.16970/) (2025)
- [Survey on Deep Fuzzy Systems in regression applications: a view on interpretability](https://mlsanity.com/paper/2209.04230/) (2022)
- [Higher order quantum reservoir computing for non-intrusive reduced-order models](https://mlsanity.com/paper/2407.21602/) (2024)
- [OptFormer: Optical Flow-Guided Attention and Phase Space Reconstruction for SST Forecasting](https://mlsanity.com/paper/2601.06078/) (2025)

## Abstract (arXiv)

This letter adopts long short-term memory(LSTM) to predict sea surface temperature(SST), which is the first attempt, to our knowledge, to use recurrent neural network to solve the problem of SST prediction, and to make one week and one month daily prediction. We formulate the SST prediction problem as a time series regression problem. LSTM is a special kind of recurrent neural network, which introduces gate mechanism into vanilla RNN to prevent the vanished or exploding gradient problem. It has strong ability to model the temporal relationship of time series data and can handle the long-term dependency problem well. The proposed network architecture is composed of two kinds of layers: LSTM layer and full-connected dense layer. LSTM layer is utilized to model the time series relationship. Full-connected layer is utilized to map the output of LSTM layer to a final prediction. We explore the optimal setting of this architecture by experiments and report the accuracy of coastal seas of China to confirm the effectiveness of the proposed method. In addition, we also show its online updated characteristics.