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Volume 10, issue 9 | Copyright
Geosci. Model Dev., 10, 3391-3409, 2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

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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Cited articles
Akaike, H.: A new look at the statistical identification model, IEEE T. Automat. Contr., 19, 716–723,, 1974.
Alfieri, L., Salamon, P., Bianchi, A., Neal, J., Bates, P., and Feyen, L.: Advances in pan-European flood hazard mapping, Hydrol. Process., 28, 4067–4077, 10.1002/hyp.9947, 2014.
Alfieri, L., Bisselink, B., Dottori, F., Naumann, G., Roo, A., Salamon, P., Wyser, K., and Feyen, L.: Global projections of river flood risk in a warmer world, Earth's Future, 5, 171–182, 2017.
Arellano, C. and Dahyot, R.: Robust ellipse detection with Gaussian mixture models, Pattern Recognit., 58, 12–26,, 2016.
Aronica, G. T., Franza, F., Bates, P. D., and Neal, J. C.: Probabilistic evaluation of flood hazard in urban areas using Monte Carlo simulation, Hydrol. Process., 26, 3962–3972,, 2012.
Publications Copernicus
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...