Harnessing generative AI for enhanced disaster management: a systematic review
In the consistently evolving artificial intelligence (AI) and large language models (LLMs), many organizations adopt these technologies’ capabilities to solve and assist core operations in many industries. In disaster areas, well-known organizations in disaster management try to shift their focus to...
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| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2025-06-01
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| Series: | Big Earth Data |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/20964471.2025.2521157 |
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| Summary: | In the consistently evolving artificial intelligence (AI) and large language models (LLMs), many organizations adopt these technologies’ capabilities to solve and assist core operations in many industries. In disaster areas, well-known organizations in disaster management try to shift their focus to apply the potential capabilities of AI and LLM to support disaster management. As AI and LLM continue to develop, this research aims to perform a structured summarization process to identify their current trend that can assist the disaster management process using a systematic review approach. The study follows the guidelines of PRISMA to ensure transparency in the review results. The findings highlighted the outstanding benefits of AI and LLM and the introduction of integrated technologies to facilitate disaster management, which can eventually mitigate disaster impacts and casualties. The refined results also proposed the technologies’ benefits in assisting the decision support process, creating a business continuity plan, and detecting early warnings. However, ethics and transparency remain the main concerns in fully implementing AI and LLM in disaster management operations. Moreover, the SWOT analysis, represented by the TOWS matrix, was also performed to identify core strategies based on internal and external factors for assisting the disaster management operations. |
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| ISSN: | 2096-4471 2574-5417 |