Risk analysis for real-time flood control operation of a multi-reservoir system using a dynamic Bayesian network. Chen, J., Zhong, P., An, R., Zhu, F., & Xu, B. Environmental Modelling & Software, Elsevier, 10, 2018.
Risk analysis for real-time flood control operation of a multi-reservoir system using a dynamic Bayesian network [link]Website  abstract   bibtex   
This paper proposes a model for risk analysis of real-time flood control operation of a multi-reservoir system using a dynamic Bayesian network. The proposed model consists of three components: Monte Carlo simulations, dynamic Bayesian network establishing, and risk-informed inference for decision making. The Monte Carlo simulations provide basic data inputs for the dynamic Bayesian network establishing using the historical floods and operation models of the multi-reservoir system. The dynamic Bayesian network is built with expert knowledge and the relationships among the uncertainties. The component of risk-informed inference for decision making is to provide risk information about the operation schedules using the trained dynamic Bayesian network. We apply the proposed model to a multi-reservoir system in China. The results show that the proposed method has a capability for bi-directional inferences and can be served as a risk-informed decision-making tool under uncertainties in the real-time flood control operation of a multi-reservoir system.
@article{
 title = {Risk analysis for real-time flood control operation of a multi-reservoir system using a dynamic Bayesian network},
 type = {article},
 year = {2018},
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 month = {10},
 publisher = {Elsevier},
 day = {26},
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 abstract = {This paper proposes a model for risk analysis of real-time flood control operation of a multi-reservoir system using a dynamic Bayesian network. The proposed model consists of three components: Monte Carlo simulations, dynamic Bayesian network establishing, and risk-informed inference for decision making. The Monte Carlo simulations provide basic data inputs for the dynamic Bayesian network establishing using the historical floods and operation models of the multi-reservoir system. The dynamic Bayesian network is built with expert knowledge and the relationships among the uncertainties. The component of risk-informed inference for decision making is to provide risk information about the operation schedules using the trained dynamic Bayesian network. We apply the proposed model to a multi-reservoir system in China. The results show that the proposed method has a capability for bi-directional inferences and can be served as a risk-informed decision-making tool under uncertainties in the real-time flood control operation of a multi-reservoir system.},
 bibtype = {article},
 author = {Chen, Juan and Zhong, Ping-An and An, Ru and Zhu, Feilin and Xu, Bin},
 journal = {Environmental Modelling & Software}
}

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