Efficient SST prediction in the Red Sea using hybrid deep learning-based approach

Earth and Planetary Sciences

• Air Temperature 12%
• Approach 37%
• Architecture 12%
• Atmospheric Circulation 12%
• Datum 12%
• Dependence 12%
• Flexibility 12%
• Hybrid 37%
• Improvement 12%
• Kriging 37%
• Machine Learning 12%
• Memory 37%
• Model 37%
• Network 37%
• Ocean 12%
• Pattern Recognition 12%
• Physical Phenomena 12%
• Prediction 50%
• Red Sea 25%
• Region 12%
• Relative Humidity 12%
• Sea 12%
• Sea Surface Temperature 100%
• Temperature 12%
• Time Series 25%
• Wind Velocity 12%

Computer Science

• Driven Approach 12%
• Efficient Surface 12%
• Ensemble Learning 12%
• Feature Extraction 12%
• Networks 37%
• Physical Phenomenon 12%
• Prediction Accuracy 12%
• Relative Humidity 12%
• Roles 12%
• Series Prediction 12%
• Simulation Mode 37%

Chemical Engineering

• Deep Learning 25%
• Learning System 12%
• Temperature 12%