Harnessing emerging technologies to address data gaps in natural disaster risk management: A conceptual framework and applications

Abstract

Natural disasters have increasingly disrupted and devastated economic and social systems worldwide. Emerging technologies, such as artificial intelligence and machine learning have demonstrated significant potential for enhancing natural disaster risk management (DRM). However, existing studies predominantly emphasize practical technological applications, focusing narrowly on specific use cases. Only a limited number of conceptual frameworks have been proposed, each grounded in distinct thematic perspectives, such as principle-technology integration, life-cycle application, or operational reliability. Critically, there remains a notable gap regarding a comprehensive framework that systematically addresses data challenges inherent in DRM. This paper proposes a data-governance-oriented conceptual framework that classifies three major data challenges—insufficient data, poor data quality, and limited application, across both objective and subjective dimensions of risk management. By integrating practical case studies, the framework illustrates how emerging technologies can systematically mitigate these challenges. Furthermore, this paper identifies new data-related risks introduced by emerging technologies. By offering a closed-loop structure that aligns internal data governance with evolving DRM needs, this work contributes novel and actionable approaches to guiding the integration of emerging technologies into disaster risk management practice.

Graphical abstract

Keywords

emerging technology / data challenge / natural disaster risk / objective risk / subjective risk

References

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