Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Bayesian Networks and Decision Graphs
Bayesian Networks and Decision Graphs
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Probabilistic Networks and Expert Systems
Probabilistic Networks and Expert Systems
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
Environmental Modelling & Software
Public participation modelling using Bayesian networks in management of groundwater contamination
Environmental Modelling & Software
Environmental Modelling & Software
Assessment of nitrate contamination of groundwater using lumped-parameter models
Environmental Modelling & Software
Reforestation planning using Bayesian networks
Environmental Modelling & Software
Environmental Modelling & Software
The Analytical Hierarchy Process for contaminated land management
Advanced Engineering Informatics
Environmental Modelling & Software
Challenging beliefs through multi-level participatory modelling in Indonesia
Environmental Modelling & Software
Position Paper: Modelling with stakeholders
Environmental Modelling & Software
Environmental Modelling & Software
A methodology for the design and development of integrated models for policy support
Environmental Modelling & Software
A DSS generator for multiobjective optimisation of spreadsheet-based models
Environmental Modelling & Software
Modeling net ecosystem metabolism with an artificial neural network and Bayesian belief network
Environmental Modelling & Software
Review: Bayesian networks in environmental modelling
Environmental Modelling & Software
Environmental Modelling & Software
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An integrated methodology, based on Bayesian belief network (BBN) and evolutionary multi-objective optimization (EMO), is proposed for combining available evidence to help water managers evaluate implications, including costs and benefits of alternative actions, and suggest best decision pathways under uncertainty. A Bayesian belief network is a probabilistic graphical model that represents a set of variables and their probabilistic relationships, which also captures historical information about these dependencies. In complex applications where the task of defining the network could be difficult, the proposed methodology can be used in validation of the network structure and the parameters of the probabilistic relationship. Furthermore, in decision problems where it is difficult to choose appropriate combinations of interventions, the states of key variables under the full range of management options cannot be analyzed using a Bayesian belief network alone as a decision support tool. The proposed optimization method is used to deal with complexity in learning about actions and probabilities and also to perform inference. The optimization algorithm generates the state variable values which are fed into the Bayesian belief network. It is possible then to calculate the probabilities for all nodes in the network (belief propagation). Once the probabilities of all the linked nodes have been updated, the objective function values are returned to the optimization tool and the process is repeated. The proposed integrated methodology can help in dealing with uncertainties in decision making pertaining to human behavior. It also eliminates the shortcoming of Bayesian belief networks in introducing boundary constraints on probability of state values of the variables. The effectiveness of the proposed methodology is examined in optimum management of groundwater contamination risks for a well field capture zone outside Copenhagen city.