In the ever-evolving field of climate data management, the pressing need for accurate, real-time data to mitigate the effects of severe weather phenomena has never been more critical. This study presents the development of an advanced Internet-of-Things (IoT) sensor architecture aimed at enhancing climate data management; a real-life industrial scenario in the Dominican Republic was targeted, specifically, a region frequently impacted by severe atmospheric phenomena. Specifically, our solution integrates (a) machine learning for data validation and (b) blockchain-oriented computing for ensuring data integrity; the resulting system is designed to provide reliable, real-time climate data crucial for disaster risk reduction, emergency planning, and climate change policies. Our field evaluation suggests that the potential to transform climate data management practices in our scenario is significant, offering a model that could be replicated in similar contexts globally.
No takes yet. Share an insight, caveat, or question.
Radocchia et al. (2025) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: