Fusion, propagation, and structuring in belief networks
Artificial Intelligence
The sensitivity of belief networks to imprecise probabilities: an experimental investigation
Artificial Intelligence - Special volume on empirical methods
Bayesian network models for generation of crisis management training scenarios
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
The Handbook of Brain Theory and Neural Networks
The Handbook of Brain Theory and Neural Networks
Introduction to Bayesian Networks
Introduction to Bayesian Networks
Bayesian Networks and participatory modelling in water resource management
Environmental Modelling & Software
Public participation modelling using Bayesian networks in management of groundwater contamination
Environmental Modelling & Software
Parameterisation and evaluation of a Bayesian network for use in an ecological risk assessment
Environmental Modelling & Software
Bayesian Networks and Decision Graphs
Bayesian Networks and Decision Graphs
Environmental Modelling & Software
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
Bayesian networks for patient monitoring
Artificial Intelligence in Medicine
Good practice in Bayesian network modelling
Environmental Modelling & Software
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Wastewater treatment is a complicated dynamic process, the effectiveness of which is affected by microbial, chemical, and physical factors. At present, predicting the effluent quality of wastewater treatment systems is difficult because of complex biological reaction mechanisms that vary with both time and the physical attributes of the system. Bayesian networks are useful for addressing uncertainties in artificial intelligence applications. Their powerful inferential capability and convenient decision support mechanisms provide flexibility and applicability for describing and analyzing factors affecting wastewater treatment systems. In this study, a Bayesian network-based approach for modeling and predicting a wastewater treatment system based on Modified Sequencing Batch Reactor (MSBR) was proposed. Using the presented approach, a Bayesian network model for MSBR can be constructed using experiential information and physical data relating to influent loads, operating conditions, and effluent concentrations. Additionally, MSBR prediction analysis, wherein effluent concentration can be predicted from influent loads and operational conditions, can be performed. This approach can be applied, with minimal modifications, to other types of wastewater treatment plants.