Use of artificial intelligence to improve resilience and preparedness against adverse flood events

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dc.contributor.authorSaravi, Sara
dc.contributor.authorKalawsky, Roy
dc.contributor.authorJoannou, Demetrios
dc.contributor.authorRivas Casado, Monica
dc.contributor.authorFu, Guangtao
dc.contributor.authorMeng, Fanlin
dc.date.accessioned2019-05-10T15:48:49Z
dc.date.available2019-05-10T15:48:49Z
dc.date.issued2019-05-09
dc.description.abstractThe main focus of this paper is the novel use of Artificial Intelligence (AI) in natural disaster, more specifically flooding, to improve flood resilience and preparedness. Different types of flood have varying consequences and are followed by a specific pattern. For example, a flash flood can be a result of snow or ice melt and can occur in specific geographic places and certain season. The motivation behind this research has been raised from the Building Resilience into Risk Management (BRIM) project, looking at resilience in water systems. This research uses the application of the state-of-the-art techniques i.e., AI, more specifically Machin Learning (ML) approaches on big data, collected from previous flood events to learn from the past to extract patterns and information and understand flood behaviours in order to improve resilience, prevent damage, and save lives. In this paper, various ML models have been developed and evaluated for classifying floods, i.e., flash flood, lakeshore flood, etc. using current information i.e., weather forecast in different locations. The analytical results show that the Random Forest technique provides the highest accuracy of classification, followed by J48 decision tree and Lazy methods. The classification results can lead to better decision-making on what measures can be taken for prevention and preparedness and thus improve flood resilienceen_UK
dc.identifier.citationSaravi S, Kalawsky R, Joannou D, Rivas-Casado M, Fu G and Meng F., Use of artificial intelligence to improve resilience and preparedness against adverse flood events, Water (Switzerland), Volume 11, Issue 5, 2019, Article No. 973.en_UK
dc.identifier.issn2073-4441
dc.identifier.urihttps://doi.org/10.3390/w11050973
dc.identifier.urihttp://dspace.lib.cranfield.ac.uk/handle/1826/14147
dc.language.isoenen_UK
dc.publisherMDPIen_UK
dc.relation.ispartofseries;973
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectArtificial Intelligenceen_UK
dc.subjectmachine learningen_UK
dc.subjectflooden_UK
dc.subjectpreparednessen_UK
dc.subjectflood resilienceen_UK
dc.subjectresilienceen_UK
dc.titleUse of artificial intelligence to improve resilience and preparedness against adverse flood eventsen_UK
dc.typeArticleen_UK

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