Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Real-world applications of Bayesian networks
Communications of the ACM
Expert Systems and Probabiistic Network Models
Expert Systems and Probabiistic Network Models
A Guide to the Literature on Learning Probabilistic Networks from Data
IEEE Transactions on Knowledge and Data Engineering
Reservoir operation using the neural network and fuzzy systems for dam control and operation support
Advances in Engineering Software
Learning Bayesian Networks
Editorial: Bayesian networks in water resource modelling and management
Environmental Modelling & Software
Bayesian Networks and participatory modelling in water resource management
Environmental Modelling & Software
Bayesian networks in planning a large aquifer in Eastern Mancha, Spain
Environmental Modelling & Software
Coupling real-time control and socio-economic issues in participatory river basin planning
Environmental Modelling & Software
Public participation modelling using Bayesian networks in management of groundwater contamination
Environmental Modelling & Software
An investigation of a hyperheuristic genetic algorithm applied to a trainer scheduling problem
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Water reservoir control under economic, social and environmental constraints
Automatica (Journal of IFAC)
Environmental Modelling & Software
Environmental Modelling & Software
Development of reservoir operation policies considering variable agricultural water demands
Expert Systems with Applications: An International Journal
Environmental Modelling & Software
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Review: Bayesian networks in environmental modelling
Environmental Modelling & Software
Environmental Modelling & Software
Environmental Modelling & Software
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In this paper, a Bayesian Network (BN) is utilized for developing monthly operating rules for a cascade system of reservoirs which is mainly aimed to control floods and supply irrigation needs. BN is trained and verified using the results of a reservoir operation optimization model, which optimizes monthly releases from cascade reservoirs. The inputs of the BN are monthly inflows, reservoir storages at the beginning of the month, and downstream water demands. The trained BN provides the probability distribution functions of reservoirs' releases for each set of input data. The long-term optimization model in monthly scale is formulated to minimize the expected flood and agricultural water deficit damages. The optimization model is developed using an extended version of the Varying chromosome Length Genetic Algorithm (VLGA-II). To incorporate reservoir preparedness for controlling the probable floods in each month, damages associated with floods with different return periods have been considered in the optimization model. For this purpose, a short-term optimization model which provides the optimal hourly releases during floods is utilized and linked to a flood damage estimation model. Damages due to deficit in supplying agricultural water demands are also calculated based on the functions of crop yield responses to deficit irrigation. The developed models are applied to the cascade system of the Dez and Bakhtiari Reservoirs in Southwest of Iran. The result of the trained BN is compared with the rules developed using classical and fuzzy linear regressions and it is shown that the total damage obtained by the BN-based operating rules is about 60 percent less than the total damage obtained using the fuzzy and classical regression analyses. The average relative error in estimating optimal releases is also reduced about 30 percent by using the BN-based rules.