Risk analysis for real-time flood control operation of a multi-reservoir system using a dynamic Bayesian network. (January 2019) - Chen, Juan; Zhong, Ping-An; An, Ru; Zhu, Feilin; Xu, Bin
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. Highlights: Proposing a dynamic Bayesian network for risk analysis of a multi-reservoir system. Performing prediction of risks under uncertainties using Bayesian networks. Performing diagnosis of reasons under given risk events using Bayesian networks.