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dc.contributor.authorTien Bui, Dieu
dc.contributor.authorHoang, Nhat-Duc
dc.date.accessioned2018-02-23T10:12:53Z
dc.date.available2018-02-23T10:12:53Z
dc.date.created2017-09-09T08:54:54Z
dc.date.issued2017
dc.identifier.citationGeoscientific Model Development. 2017, 10 (9), 3391-3409.nb_NO
dc.identifier.issn1991-959X
dc.identifier.urihttp://hdl.handle.net/11250/2486684
dc.description.abstractIn this study, a probabilistic model,named as BayGmmKda, is proposed for flood susceptibility assessment in a study area in central Vietnam. The new model is a Bayesian framework constructed by a combination of a Gaussian mixture model (GMM), radial-basisfunction Fisher discriminant analysis (RBFDA), and a geographic information system (GIS) database. In the Bayesian framework, GMM is used for modeling the data distribution of flood-influencing factors in the GIS database, whereas RBFDA is utilized to construct a latent variable that aims at enhancing the model performance. As a result, the posterior probabilistic output of the BayGmmKda model is used as flood susceptibility index. Experiment results showed that the proposed hybrid framework is superior to other benchmark models, including the adaptive neuro-fuzzy inference system and the support vector machine. To facilitate the model implementation, a software program of BayGmmKda has been developed in MATLAB. The BayGmmKda program can accurately establish a flood susceptibility map for the study region. Accordingly, local authorities can overlay this susceptibility map onto various land-use maps for the purpose of land-use planning or management.nb_NO
dc.language.isoengnb_NO
dc.relation.urihttps://www.geosci-model-dev.net/10/3391/2017/gmd-10-3391-2017.html
dc.rightsNavngivelse 3.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/deed.no*
dc.titleA Bayesian framework based on a Gaussian mixture model and radial-basis-function Fisher discriminant analysis (BayGmmKda V1.1) for spatial prediction of floodsnb_NO
dc.typeJournal articlenb_NO
dc.typePeer reviewednb_NO
dc.description.versionpublishedVersionnb_NO
dc.rights.holderAuthor(s)nb_NO
dc.source.pagenumber3391-3409nb_NO
dc.source.volume10nb_NO
dc.source.journalGeoscientific Model Developmentnb_NO
dc.source.issue9nb_NO
dc.identifier.doi10.5194/gmd-10-3391-2017
dc.identifier.cristin1492337
cristin.unitcode222,57,1,0
cristin.unitnameInstitutt for økonomi og IT
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode2


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