Assessing spatial likelihood of flooding hazard using naïve Bayes and GIS: a case study in Bowen Basin, Australia

Abstract

Flooding hazard evaluation is the basis of flooding risk assessment which has significances to natural environment, human life and social economy. This study develops a spatial framework integrating naïve Bayes (NB) and geographic information system (GIS) to assess flooding hazard at regional scale. The methodology was demonstrated in the Bowen Basin in Australia as a case study. The inputs into the framework are five indices: elevation, slope, soil water retention, drainage proximity and density. They were derived from spatial data processed in ArcGIS. NB as a simplified and efficient type of Bayesian methods was used, with the assistance of remotely sensed flood inundation extent in the sampling process, to infer flooding probability on a cell-by-cell basis over the study area. A likelihood-based flooding hazard map was output from the GIS-based framework. The results reveal elevation and slope have more significant impacts on evaluation than other input indices. Area of high likelihood of flooding hazard is mainly located in the west and the southwest where there is a high water channel density, and along the water channels in the east of the study area. High likelihood of flooding hazard covers 45 % of the total area, medium likelihood accounts for about 12 %, low and very low likelihood represents 19 and 24 %, respectively. The results provide baseline information to identify and assess flooding hazard when making adaptation strategies and implementing mitigation measures in future. The framework and methodology developed in the study offer an integrated approach in evaluation of flooding hazard with spatial distributions and indicative uncertainties. It can also be applied to other hazard assessments.

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Acknowledgments

The authors appreciate the China Scholarship Council for providing a scholarship to Rui Liu to support this research at CSIRO Land and Water (CLW). The authors also want to thank the CLW for providing the data for this study. The study was funded by National Natural Science Foundation of China (41201548), Innovation Program of Shanghai Municipal Education Commission (12YZ086) and Funding Plan of Shanghai Municipal Education Commission for the Development of Young Teachers in Colleges and Universities (shsf019). The authors are grateful to our colleague, Susan Cuddy, for reviewing the manuscript. The anonymous reviewers are acknowledged for their valuable comments.

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Liu, R., Chen, Y., Wu, J. et al. Assessing spatial likelihood of flooding hazard using naïve Bayes and GIS: a case study in Bowen Basin, Australia. Stoch Environ Res Risk Assess 30, 1575–1590 (2016). https://doi.org/10.1007/s00477-015-1198-y

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Keywords

  • MODIS
  • Inundation
  • Risk
  • Likelihood
  • Spatial uncertainty
  • Probability