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	Assessment of Catastrophic Risk Using Bayesian Network Constructed from Domain Knowledge and Spatial Data
	Li L. F. ; Wang J. F. ; Leung H. ; Jiang C. S.
	2010
关键词	Bayesian network domain knowledge risk analysis spatial data mining gis
英文摘要	Prediction of natural disasters and their consequences is difficult due to the uncertainties and complexity of multiple related factors. This article explores the use of domain knowledge and spatial data to construct a Bayesian network (BN) that facilitates the integration of multiple factors and quantification of uncertainties within a consistent system for assessment of catastrophic risk. A BN is chosen due to its advantages such as merging multiple source data and domain knowledge in a consistent system, learning from the data set, inference with missing data, and support of decision making. A key advantage of our methodology is the combination of domain knowledge and learning from the data to construct a robust network. To improve the assessment, we employ spatial data analysis and data mining to extend the training data set, select risk factors, and fine-tune the network. Another major advantage of our methodology is the integration of an optimal discretizer, informative feature selector, learners, search strategies for local topologies, and Bayesian model averaging. These techniques all contribute to a robust prediction of risk probability of natural disasters. In the flood disaster's study, our methodology achieved a better probability of detection of high risk, a better precision, and a better ROC area compared with other methods, using both cross-validation and prediction of catastrophic risk based on historic data. Our results suggest that BN is a good alternative for risk assessment and as a decision tool in the management of catastrophic risk.
出处	Risk Analysis
卷	30
期	7
页	1157-1175
收录类别	SCI
语种	英语
ISSN号	0272-4332
内容类型	SCI/SSCI论文
源URL	[http://ir.igsnrr.ac.cn/handle/311030/23639]
专题	地理科学与资源研究所_历年回溯文献
推荐引用方式 GB/T 7714	Li L. F.,Wang J. F.,Leung H.,et al. Assessment of Catastrophic Risk Using Bayesian Network Constructed from Domain Knowledge and Spatial Data. 2010.

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