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

Related subject area

Climate and Earth system modeling
Yeti 1.0: a generalized framework for constructing bottom-up emission inventories from traffic sources at road-link resolutions
Edward C. Chan, Joana Leitão, Andreas Kerschbaumer, and Timothy M. Butler
Geosci. Model Dev., 16, 1427–1444, https://doi.org/10.5194/gmd-16-1427-2023,https://doi.org/10.5194/gmd-16-1427-2023, 2023
Short summary
Analysis of systematic biases in tropospheric hydrostatic delay models and construction of a correction model
Haopeng Fan, Siran Li, Zhongmiao Sun, Guorui Xiao, Xinxing Li, and Xiaogang Liu
Geosci. Model Dev., 16, 1345–1358, https://doi.org/10.5194/gmd-16-1345-2023,https://doi.org/10.5194/gmd-16-1345-2023, 2023
Short summary
A new precipitation emulator (PREMU v1.0) for lower-complexity models
Gang Liu, Shushi Peng, Chris Huntingford, and Yi Xi
Geosci. Model Dev., 16, 1277–1296, https://doi.org/10.5194/gmd-16-1277-2023,https://doi.org/10.5194/gmd-16-1277-2023, 2023
Short summary
Simulating marine neodymium isotope distributions using Nd v1.0 coupled to the ocean component of the FAMOUS–MOSES1 climate model: sensitivities to reversible scavenging efficiency and benthic source distributions
Suzanne Robinson, Ruza F. Ivanovic, Lauren J. Gregoire, Julia Tindall, Tina van de Flierdt, Yves Plancherel, Frerk Pöppelmeier, Kazuyo Tachikawa, and Paul J. Valdes
Geosci. Model Dev., 16, 1231–1264, https://doi.org/10.5194/gmd-16-1231-2023,https://doi.org/10.5194/gmd-16-1231-2023, 2023
Short summary
CMIP6 simulations with the compact Earth system model OSCAR v3.1
Yann Quilcaille, Thomas Gasser, Philippe Ciais, and Olivier Boucher
Geosci. Model Dev., 16, 1129–1161, https://doi.org/10.5194/gmd-16-1129-2023,https://doi.org/10.5194/gmd-16-1129-2023, 2023
Short summary

Cited articles

Akaike, H.: A new look at the statistical identification model, IEEE T. Automat. Contr., 19, 716–723, https://doi.org/10.1109/TAC.1974.1100705, 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, https://doi.org/10.1016/j.patcog.2016.01.017, 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, https://doi.org/10.1002/hyp.8370, 2012.
Download
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.