Gorde:
| Egile Nagusiak: | , |
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| Formatua: | Recurso digital |
| Hizkuntza: | |
| Argitaratua: |
Zenodo
2025
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| Sarrera elektronikoa: | https://doi.org/10.5281/zenodo.17838470 |
| Etiketak: |
Etiketa erantsi
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Aurkibidea:
- Dynamic human-environment systems present intricate challenges for predictive modeling due to their inherent complexity, non-linearity, and the constantly evolving interplay between anthropogenic activities and natural processes. Traditional analytical approaches often struggle to capture the multifaceted spatiotemporal dependencies and feedback loops characteristic of these systems. This paper introduces a comprehensive framework for Coupled Spatiotemporal AI (CSAI), designed to enhance predictive power by explicitly modeling and integrating both spatial and temporal dynamics, alongside the critical human-environment feedback mechanisms. We review existing AI methodologies, including deep learning architectures and advanced data fusion techniques, and propose a novel architectural paradigm that leverages dynamic graph neural networks, recurrent neural networks, and attention mechanisms to process heterogeneous spatiotemporal data streams. The proposed CSAI framework aims to move beyond mere correlation to infer causal relationships and anticipate future states with higher fidelity. We discuss the potential applications across diverse domains, such as climate change impact assessment, urban planning, disaster management, and public health, demonstrating how CSAI can provide actionable insights for proactive decision-making. Furthermore, the paper addresses the methodological challenges related to data heterogeneity, model interpretability, and computational scalability, outlining future research directions to advance the practical deployment and ethical governance of these sophisticated AI systems. By unlocking the predictive potential of coupled spatiotemporal AI, we envision a future where human societies can better understand, adapt to, and sustainably manage their complex interactions with the environment.