Articles | Volume 10, issue 9
https://doi.org/10.5194/gmd-10-3391-2017
https://doi.org/10.5194/gmd-10-3391-2017
Model description paper
 | 
14 Sep 2017
Model description paper |  | 14 Sep 2017

A Bayesian framework based on a Gaussian mixture model and radial-basis-function Fisher discriminant analysis (BayGmmKda V1.1) for spatial prediction of floods

Dieu Tien Bui and Nhat-Duc Hoang

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Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Nhat-Duc Hoang on behalf of the Authors (04 Apr 2017)  Author's response   Manuscript 
ED: Referee Nomination & Report Request started (12 May 2017) by Jeffrey Neal
RR by Anonymous Referee #2 (26 May 2017)
RR by Anonymous Referee #1 (27 May 2017)
ED: Reconsider after major revisions (12 Jun 2017) by Jeffrey Neal
AR by Nhat-Duc Hoang on behalf of the Authors (25 Jun 2017)  Author's response   Manuscript 
ED: Publish subject to minor revisions (Editor review) (13 Jul 2017) by Jeffrey Neal
AR by Nhat-Duc Hoang on behalf of the Authors (17 Jul 2017)  Author's response   Manuscript 
ED: Publish subject to technical corrections (10 Aug 2017) by Jeffrey Neal
AR by Nhat-Duc Hoang on behalf of the Authors (12 Aug 2017)  Author's response   Manuscript 
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Short summary
A probabilistic model, named BayGmmKda, is proposed for flood susceptibility assessment in central Vietnam. The model is a combination of Gaussian mixture model and radial-basis-function Fisher discriminant analysis. A geographic information system (GIS) database has been established for model construction. The proposed model can accurately establish a flood susceptibility map for the study region. Local authorities can use this map for land-use planning.