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A Bayesian Method to Mine Spatial Data Sets to Evaluate the Vulnerability of Human Beings to Catastrophic Risk

renchunxiao 添加于 2012/4/24 15:12:45  1416次阅读 | 0次推荐 | 0个评论

Vulnerability of human beings exposed to a catastrophic disaster is affected by multiple factors that include hazard intensity, environment, and individual characteristics. The traditional approach to vulnerability assessment, based on the aggregate‐area method and unsupervised learning, cannot incorporate spatial information; thus, vulnerability can be only roughly assessed. In this article, we propose Bayesian network (BN) and spatial analysis techniques to mine spatial data sets to evaluate the vulnerability of human beings. In our approach, spatial analysis is leveraged to preprocess the data; for example, kernel density analysis (KDA) and accumulative road cost surface modeling (ARCSM) are employed to quantify the influence of geofeatures on vulnerability and relate such influence to spatial distance. The knowledge‐ and data‐based BN provides a consistent platform to integrate a variety of factors, including those extracted by KDA and ARCSM to model vulnerability uncertainty. We also consider the model's uncertainty and use the Bayesian model average and Occam's Window to average the multiple models obtained by our approach to robust prediction of the risk and vulnerability. We compare our approach with other probabilistic models in the case study of seismic risk and conclude that our approach is a good means to mining spatial data sets for evaluating vulnerability.

作 者:Li, Lianfa; Wang, Jinfeng; Leung, Hareton; Zhao, Sisi
期刊名称: Risk Analysis
期卷页: 第卷 第期 ~页
学科领域:地球科学 » 地理学 » 自然地理学
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原文链接:http://onlinelibrary.wiley.com/doi/10.1111/j.1539-6924.2012.01790.x/abstract
DOI: 10.1111/j.1539-6924.2012.01790.x
ISBN: 1539-6924
关键词: Bayesian network;
相关报道: http://paper.sciencenet.cn/htmlpaper/20124259422512923511.shtm
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