Deep learning for spatiotemporal forecasting in Earth system science: a review

Keyphrases

• Deep Learning 100%
• Earth System Science 100%
• Spatiotemporal Prediction 100%
• Deep Learning Architectures 75%
• Spatio-temporal Data 50%
• Physical Knowledge 50%
• Technical Innovation 25%
• Spatiotemporal 25%
• Environmental Monitoring 25%
• Strong Potential 25%
• Synergize 25%
• Model Integration 25%
• Specific Needs 25%
• Uncertainty Quantification 25%
• Scale Dependence 25%
• Modeling Prediction 25%
• Hydrological Modeling 25%
• Air Quality Modeling 25%
• Data Challenge 25%
• Disaster Management 25%
• Weather Prediction 25%
• Scientific Domain 25%
• Physical Approach 25%
• Latest Advancements 25%
• Model Interpretability 25%
• Dependency Model 25%
• Model Explainability 25%
• Data Scaling 25%
• Recent Innovations 25%
• Climate Modeling 25%
• Model Generalization 25%

Earth and Planetary Sciences

• Earth System Science 100%
• Climate Modeling 25%
• Renewable Energy 25%
• Environmental Monitoring 25%
• Hydrology Models 25%
• Weather Condition 25%
• Air Quality Modeling 25%
• Uncertainty Modeling 25%
• Disaster Management 25%
• Oceanography 25%
• Hybrid Model 25%
• Interpretability 25%
• Transfer Learning 25%

Computer Science

• Deep Learning Method 100%
• Spatiotemporal Data 28%
• Interpretability 14%
• Future Direction 14%
• Generalizability 14%
• Data Handling 14%
• Transfer Learning 14%
• Domain Adaptation 14%
• Weather Condition 14%