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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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-06-01
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| Series: | Big Earth Data |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/20964471.2025.2521157 |
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| author | Kumpol Saengtabtim Natt Leelawat Rujapa Aumnoysombat Mahsa Adelifar Natcha Saengwongwattana Gritaya Suktavornprasit Anawat Suppasri Fumihiko Imamura Jing Tang |
| author_facet | Kumpol Saengtabtim Natt Leelawat Rujapa Aumnoysombat Mahsa Adelifar Natcha Saengwongwattana Gritaya Suktavornprasit Anawat Suppasri Fumihiko Imamura Jing Tang |
| author_sort | Kumpol Saengtabtim |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-4aef2cadc0e1474c8b2a616f019594db |
| institution | Kabale University |
| issn | 2096-4471 2574-5417 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Big Earth Data |
| spelling | doaj-art-4aef2cadc0e1474c8b2a616f019594db2025-08-20T03:24:55ZengTaylor & Francis GroupBig Earth Data2096-44712574-54172025-06-0112510.1080/20964471.2025.2521157Harnessing generative AI for enhanced disaster management: a systematic reviewKumpol Saengtabtim0Natt Leelawat1Rujapa Aumnoysombat2Mahsa Adelifar3Natcha Saengwongwattana4Gritaya Suktavornprasit5Anawat Suppasri6Fumihiko Imamura7Jing Tang8Department of Industrial Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok, ThailandDepartment of Industrial Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok, ThailandInternational School of Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok, ThailandInternational School of Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok, ThailandInternational School of Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok, ThailandInternational School of Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok, ThailandInternational Research Institute of Disaster Science, Tohoku University, Sendai, JapanInternational Research Institute of Disaster Science, Tohoku University, Sendai, JapanDisaster and Risk Management Information Systems Research Unit, Chulalongkorn University, Bangkok, ThailandIn 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.https://www.tandfonline.com/doi/10.1080/20964471.2025.2521157Artificial intelligencedisaster managementdisastersgenerative artificial intelligencelarge language modelssystematic review |
| spellingShingle | Kumpol Saengtabtim Natt Leelawat Rujapa Aumnoysombat Mahsa Adelifar Natcha Saengwongwattana Gritaya Suktavornprasit Anawat Suppasri Fumihiko Imamura Jing Tang Harnessing generative AI for enhanced disaster management: a systematic review Big Earth Data Artificial intelligence disaster management disasters generative artificial intelligence large language models systematic review |
| title | Harnessing generative AI for enhanced disaster management: a systematic review |
| title_full | Harnessing generative AI for enhanced disaster management: a systematic review |
| title_fullStr | Harnessing generative AI for enhanced disaster management: a systematic review |
| title_full_unstemmed | Harnessing generative AI for enhanced disaster management: a systematic review |
| title_short | Harnessing generative AI for enhanced disaster management: a systematic review |
| title_sort | harnessing generative ai for enhanced disaster management a systematic review |
| topic | Artificial intelligence disaster management disasters generative artificial intelligence large language models systematic review |
| url | https://www.tandfonline.com/doi/10.1080/20964471.2025.2521157 |
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