NDLI: A fuzzy graph evolved by a new adaptive Bayesian framework and its applications in natural hazards

Content Provider	Springer Nature Link
Author	Bai, Chengzu Zhang, Ren Qian, Longxia Wu, Yaning
Copyright Year	2017
Abstract	A formal Bayesian approach that uses the Markov chain Monte Carlo method to estimate the uncertainties of natural hazards has attracted significant attention in recent years, and a fuzzy graph can be considered an estimation of the relationship that we want to know in risk systems. However, the challenge with such approaches is to sufficiently consider uncertainty without much prior knowledge and adequate measurement. This paper proposes a new adaptive Bayesian framework that is based on the conventional Bayesian scheme and the optimal information diffusion model to more precisely calculate the conditional probabilities in the fuzzy graph for recognizing relationships and estimating uncertainty in natural disasters with scant data. This methodology is applied to study the relationship between the earthquake’s magnitude and the isoseismal area with strong-motion earthquake observations. It is also compared with other techniques, including classic Bayesian regression and artificial neural networks. The results show that the new method achieves better performance than do the main existing methods with incomplete data.
Starting Page	899
Ending Page	918
Page Count	20
File Format	PDF
ISSN	0921030X
Journal	Natural Hazards
Volume Number	87
Issue Number	2
e-ISSN	15730840
Language	English
Publisher	Springer Netherlands
Publisher Date	2017-03-14
Publisher Place	Dordrecht
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Bayesian estimation Catastrophic relationship recognition Fuzzy graph Information diffusion Incomplete data Natural Hazards Hydrogeology Geophysics/Geodesy Geotechnical Engineering & Applied Earth Sciences Civil Engineering Environmental Management
Content Type	Text
Resource Type	Article
Subject	Atmospheric Science Water Science and Technology

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