Abstract
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.
Highlights
Flooding is one of the most destructive natural hazards that cause heavy loss of human lives and property in immense spatial extent (Dottori et al, 2016; Komi et al, 2017)
To yield probabilistic outputs of flood susceptibility, this study proposes a Bayesian framework established on the basis of an integration of a Gaussian mixture model (GMM) and the kernel Fisher discriminant analysis (KFDA)
In the step of the training phase, GMM is constructed by the original input patterns with their corresponding labels which consist of 10 input factors and with the radial-basis-function Fisher discriminant analysis (RBFDA)-based latent factor
Summary
Flooding is one of the most destructive natural hazards that cause heavy loss of human lives and property in immense spatial extent (Dottori et al, 2016; Komi et al, 2017). New modeling approaches should be explored and investigated Given these motivations, this study proposes a novel methodology designed for achieving a high prediction accuracy as well as deriving probabilistic evaluations of flood susceptibility on a regional scale. Spatial prediction of flooding is carried out based on a statistical assumption that flooding in the future will occur under the same conditions that triggered them in the past (Tien Bui et al, 2016b). In this way, the flood prediction problem boils down to an on–off supervised classification task, where flood inventories are used to define the class of flood occurrence.
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