The application of a Dempster–Shafer-based evidential belief function in flood susceptibility mapping and comparison with frequency ratio and logistic regression methods

  • Mahyat Shafapour TehranyEmail author
  • Lalit Kumar
Original Article


Flood is one of the most common natural disasters worldwide. The aim of this study was to evaluate the application of the Dempster–Shafer-based evidential belief function (EBF) for spatial prediction of flood-susceptible areas in Brisbane, Australia. This algorithm has been tested in landslide and groundwater mapping; however, it has not been examined in flood susceptibility modelling. EBF has an advantage over other statistical methods through its capability of evaluating the impacts of all classes of every flood-conditioning factor on flooding and assessing the correlation between each factor and flooding. EBF outcomes were compared with the results of well-known statistical methods, including logistic regression (LR) and frequency ratio (FR). Flood-conditioning factor data set consisted of elevation, aspect, plan curvature, slope, topographic wetness index (TWI), geology, stream power index (SPI), soil, land use/cover, rainfall, distance from roads and distance from rivers. EBF produced the highest prediction rate (82.60%) among all the methods. The research findings may provide a useful methodology for natural hazard and land use management.


Flood Evidential belief function Susceptibility mapping GIS Australia 


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© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Ecosystem Management, School of Environmental and Rural ScienceUniversity of New EnglandArmidaleAustralia
  2. 2.Geospatial Science, School of ScienceRMIT UniversityMelbourneAustralia

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