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Related Experiment Video

Updated: May 21, 2025

Investigating the Relationship between Sea Surface Chlorophyll and Major Features of the South China Sea with Satellite Information

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Published on: June 13, 2020

Sea Surface Temperature Prediction Enhanced by Exploring Spatiotemporal Correlation Based on LSTM and Gaussian

Zhenglin Li^1,2, Qingxiong Zhu², Dan Zhang³

¹School of Future Technology, Shanghai University, Shanghai 200444, China.

Sensors (Basel, Switzerland)

|March 17, 2025

Summary

Accurate sea surface temperature (SST) prediction is improved using a novel hybrid model. This framework enhances forecasting by integrating spatial and temporal data, outperforming existing methods.

Keywords:

Gaussian Process Regression probabilistic forecasting sea surface temperature spatiotemporal correlation

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Area of Science:

Oceanography and Climate Science
Artificial Intelligence in Environmental Modeling

Background:

Accurate sea surface temperature (SST) prediction is critical for marine studies, climate dynamics, and environmental forecasting.
General SST prediction models face challenges due to regional variations and complex climate phenomena.

Purpose of the Study:

To develop an improved SST prediction model addressing spatial and temporal dependencies.
To enhance the accuracy and reliability of sea surface temperature forecasts.

Main Methods:

Proposed a hybrid framework combining Long Short-Term Memory (LSTM) networks with Gaussian processes.
LSTM module captures temporal trends (long and short-term) in SST data.
Gaussian process integrates spatial dependencies from neighboring data to refine predictions and estimate uncertainty.

Main Results:

The hybrid framework demonstrated superior performance compared to state-of-the-art methods in SST prediction.
Experiments conducted on the OISST dataset, focusing on the Bohai Sea and South China Sea, validated the model's effectiveness.
The model successfully estimated prediction uncertainty, adding practical value.

Conclusions:

The Gaussian process-enhanced LSTM network offers a robust solution for accurate SST prediction.
The framework effectively models complex spatial and temporal dynamics in SST data.
This approach provides reliable SST forecasts with valuable uncertainty quantification for marine and climate applications.