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dc.contributor.authorKIM, Hwayeonen
dc.contributor.authorNAKAKITA, Eiichien
dc.date.accessioned2022-02-26T05:41:26Z-
dc.date.available2022-02-26T05:41:26Z-
dc.date.issued2021-12-
dc.identifier.urihttp://hdl.handle.net/2433/268157-
dc.description.abstractJapan has suffered from devastating flood disasters caused by localized heavy rainfall known as Guerrilla heavy rainfall recently. For reducing the damage, it is necessary to predict the risk of GHR precisely. So, we aim to propose an accurate quantitative risk prediction method. One of the importance was that the relationship between the predicted risk level and the variables was considered depending on each rain stage because the variables showed different characteristics according to the development of the convective cloud. The other one was that the variables were estimated with real wind field data by multiple Doppler radar analysis. Then, the multilinear regression was used for finding the correlation between the predicted risk level and the variables with accuracy. The accuracy of multilinear regression was estimated by a Receiver Operating Characteristic analysis. As the result, the most appropriate regression among the relevant variables was composed of reflectivity, vorticity, divergence, and updraft by multiple Doppler radar analysis. It is possible to predict the risk quantitatively with high accuracy of 90% at the early rain stage.en
dc.language.isoeng-
dc.publisher京都大学防災研究所ja
dc.publisher.alternativeDisaster Prevention Research Institute, Kyoto Universityen
dc.subjectGuerrilla heavy rainfallen
dc.subjectQuantitative Risk Predictionen
dc.subjectMultiple Doppler Radar Analysisen
dc.subject.ndc519.9-
dc.titlePredicting the Risk Level of Guerrilla Heavy Rainfall by Using the Quantitative Risk Prediction Method with Multiple Doppler Radar Analysisen
dc.typedepartmental bulletin paper-
dc.type.niitypeDepartmental Bulletin Paper-
dc.identifier.ncidAN00027784-
dc.identifier.jtitle京都大学防災研究所年報. Bja
dc.identifier.volume64-
dc.identifier.issueB-
dc.identifier.spage217-
dc.identifier.epage226-
dc.textversionpublisher-
dc.sortkey20-
dc.addressGraduate School of Engineering, Kyoto Universityen
dc.addressDisaster Prevention Research Institute, Kyoto Universityen
dc.relation.urlhttp://www.dpri.kyoto-u.ac.jp/publications/nenpo/-
dcterms.accessRightsopen access-
dc.identifier.pissn0386-412X-
dc.identifier.jtitle-alternativeDisaster Prevention Research Institute Annuals. Ben
出現コレクション:Vol.64 B

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