Application of artificial intelligence in anticipatory action: Drought and flood case study in the Lao People’s Democratic Republic

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dc.coverageLao People's Democratic Republic
dc.creatorIsaev, E.; Inthipunya, K.; Soukkaseum, P.; Jones, C. ; Riquet, D.; Jang, I.; et al.;
dc.date2024-10-31T19:50:16Z
dc.date2024-10-31T19:50:16Z
dc.date2024
dc.date2024-10-31T19:47:03Z
dc.date.accessioned2026-06-28T01:21:00Z
dc.descriptionThis technical study examines the triggering method for anticipatory action, which involves identifying key indicators or thresholds that, when reached or exceeded, prompt pre-planned interventions to mitigate the impact of an anticipated disaster. These triggers are designed to initiate timely actions before a disaster occurs. Specifically, this paper draws upon the case study on developing these triggering systems for managing agricultural drought and flood risks in the Lao People’s Democratic Republic. By transitioning from a reactive disaster response model to a proactive approach, it leverages artificial intelligence to improve the efficiency and effectiveness of anticipatory triggering methodologies, thus enhancing both the speed and dignity of disaster response efforts. The methodologies and systems developed through this analysis have significant potential for application beyond the Lao People's Democratic Republic. Their open-source and adaptable design offer valuable frameworks for other drought- and flood-prone regions in Asia and the Pacific.
dc.format48 p.
dc.formatapplication/pdf
dc.identifier978-92-5-139086-3
dc.identifierhttps://openknowledge.fao.org/handle/20.500.14283/cd2277en
dc.identifier.urihttp://hdl.handle.net/123456789/334088
dc.languageEnglish
dc.publisherFAO ;
dc.rightsFAO
dc.rightsCC BY NC SA 3.0 IGO
dc.titleApplication of artificial intelligence in anticipatory action: Drought and flood case study in the Lao People’s Democratic Republic
dc.titleTechnical study
dc.typeBooklet

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