Journal cover Journal topic
Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
Geosci. Model Dev., 10, 3391-3409, 2017
https://doi.org/10.5194/gmd-10-3391-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
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 Bui1 and Nhat-Duc Hoang2 1Geographic Information System Group, Department of Business and IT, University College of Southeast Norway (USN), Gullbringvegen 36, 3800, Bø i Telemark, Norway
2Faculty of Civil Engineering, Institute of Research and Development, Duy Tan University, P809 – K7/25 Quang Trung, Danang, Vietnam
Abstract. In 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-basis-function 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.

Citation: Tien Bui, D. and Hoang, N.-D.: A Bayesian framework based on a Gaussian mixture model and radial-basis-function Fisher discriminant analysis (BayGmmKda V1.1) for spatial prediction of floods, Geosci. Model Dev., 10, 3391-3409, https://doi.org/10.5194/gmd-10-3391-2017, 2017.
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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.
A probabilistic model, named BayGmmKda, is proposed for flood susceptibility assessment in...
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