Integrating Entropy‐Based Naïve Bayes and GIS for Spatial Evaluation of Flood Hazard |
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| Authors: | Rui Liu Yun Chen Jianping Wu Lei Gao Damian Barrett Tingbao Xu Xiaojuan Li Linyi Li Chang Huang Jia Yu |
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| Affiliation: | 1. Beijing Laboratory of Water Resource Security, Capital Normal University, Beijing, China;2. CSIRO Land and Water, Canberra, ACT, Australia;3. Key Laboratory of Geographic Information Science (Ministry of Education), East China Normal University, China;4. CSIRO Land and Water, Glen Osmond, SA, Australia;5. CSIRO Energy, Canberra, Australia;6. Fenner School of Environment and Society, Australian National University, Canberra, Australia;7. School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, China;8. College of Urban and Environmental Sciences, Northwest University, Xi'an, China;9. Department of Geography, Shanghai Normal University, Shanghai, China |
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| Abstract: | Regional flood risk caused by intensive rainfall under extreme climate conditions has increasingly attracted global attention. Mapping and evaluation of flood hazard are vital parts in flood risk assessment. This study develops an integrated framework for estimating spatial likelihood of flood hazard by coupling weighted naïve Bayes (WNB), geographic information system, and remote sensing. The north part of Fitzroy River Basin in Queensland, Australia, was selected as a case study site. The environmental indices, including extreme rainfall, evapotranspiration, net‐water index, soil water retention, elevation, slope, drainage proximity, and density, were generated from spatial data representing climate, soil, vegetation, hydrology, and topography. These indices were weighted using the statistics‐based entropy method. The weighted indices were input into the WNB‐based model to delineate a regional flood risk map that indicates the likelihood of flood occurrence. The resultant map was validated by the maximum inundation extent extracted from moderate resolution imaging spectroradiometer (MODIS) imagery. The evaluation results, including mapping and evaluation of the distribution of flood hazard, are helpful in guiding flood inundation disaster responses for the region. The novel approach presented consists of weighted grid data, image‐based sampling and validation, cell‐by‐cell probability inferring and spatial mapping. It is superior to an existing spatial naive Bayes (NB) method for regional flood hazard assessment. It can also be extended to other likelihood‐related environmental hazard studies. |
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| Keywords: | Inundation likelihood MODIS risk uncertainty |
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