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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.accessioned2019-10-18T15:29:32Z
dc.date.available2019-10-18T15:29:32Z
dc.date.issued2019-05-09
dc.identifier.citationSaravi, S., Kalawsky, R., Joannou, D., Rivas-Casado, M., Fu, G. and Meng, F., (2019). 'Use of artificial intelligence to improve resilience and preparedness against adverse flood events'. Water, 11(5), pp, 1-16. DOI: 10.3390/w11050973en_US
dc.identifier.issn2073-4441
dc.identifier.doi10.3390/w11050973
dc.identifier.urihttp://hdl.handle.net/10545/624221
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 resilience.en_US
dc.description.sponsorshipEPSRC for funding on BRIM (Building Resilience Into Risk Management), Ref: EP/N010329/1.en_US
dc.language.isoenen_US
dc.publisherMDPI AGen_US
dc.relation.urlhttps://repository.lboro.ac.uk/account/articles/9402362en_US
dc.relation.urlhttps://www.mdpi.com/2073-4441/11/5/973en_US
dc.subjectArtificial Intelligenceen_US
dc.subjectmachine learningen_US
dc.subjectflooden_US
dc.subjectpreparednessen_US
dc.subjectresilienceen_US
dc.subjectflood resilienceen_US
dc.titleUse of artificial intelligence to improve resilience and preparedness against adverse flood eventsen_US
dc.typeArticleen_US
dc.contributor.departmentLoughborough Universityen_US
dc.identifier.journalWateren_US
dc.source.volume11
dc.source.issue5
dc.source.beginpage973
dcterms.dateAccepted2019-05-06
refterms.dateFOA2019-10-18T15:29:33Z
dc.author.detail786958en_US


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