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July 29, 2026Neural Processing LettersOpen Access

Deep Learning: A Game Changer for Spatio‑Temporal Prediction – A Review of Methods and Applications

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Authors

SSSaeideh SamaniMVMeysam VadiatiÖKÖzgür Kişi

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Overview

Review explores deep learning methods for spatiotemporal prediction, indicating their effectiveness in physical system modeling.

Key Points

  • This review aims to evaluate deep learning's effectiveness in modeling and predicting high-dimensional spatiotemporal systems.
  • Comprehensive synthesis of deep learning architectures and applications
  • Analysis of recent literature on deep learning methods and their capabilities
  • Exploration of emerging strategies like physics-informed networks and attention mechanisms.
  • Deep learning methods effectively extract spatial and temporal patterns from complex datasets.
  • Successful applications include climate modeling, traffic forecasting, and disease outbreak detection, outperforming traditional methods.
  • Identified strategies suggest improvements in model interpretability, generalizability, and physical consistency.

Cite This Study

Samani et al. (2026) studied this question.

synapsesocial.com/papers/6a69a2a3c8da07d9defa64e3https://doi.org/10.1007/s11063-026-11874-x
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