TY - JOUR
T1 - Integrating Entropy-Based Naïve Bayes and GIS for Spatial Evaluation of Flood Hazard
AU - Liu, Rui
AU - Chen, Yun
AU - Wu, Jianping
AU - Gao, Lei
AU - Barrett, Damian
AU - Xu, Tingbao
AU - Li, Xiaojuan
AU - Li, Linyi
AU - Huang, Chang
AU - Yu, Jia
N1 - Funding Information:
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 the National Natural Science Foundation of China (41130744/D0107, 41371343, and 41501460). The authors are grateful to our colleagues, Susan Cuddy and Shenjun Yao, for reviewing the article. The anonymous reviewers are acknowledged for their valuable comments.
Publisher Copyright:
© 2016 Society for Risk Analysis
PY - 2017/4
Y1 - 2017/4
N2 - 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.
AB - 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.
KW - Inundation
KW - likelihood
KW - MODIS
KW - risk
KW - uncertainty
UR - http://www.scopus.com/inward/record.url?scp=84988805315&partnerID=8YFLogxK
U2 - 10.1111/risa.12698
DO - 10.1111/risa.12698
M3 - Article
C2 - 27663699
AN - SCOPUS:84988805315
SN - 0272-4332
VL - 37
SP - 756
EP - 773
JO - Risk Analysis
JF - Risk Analysis
IS - 4
ER -
