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Fully connected Bayesian belief networks: A modeling procedure with a case study of the Ganges river basin
Author(s) -
Varis Olli,
Rahaman Muhammad Mizanur,
Kajander Tommi
Publication year - 2012
Publication title -
integrated environmental assessment and management
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.665
H-Index - 57
eISSN - 1551-3793
pISSN - 1551-3777
DOI - 10.1002/ieam.222
Subject(s) - bayesian network , promotion (chess) , mainstream , field (mathematics) , drainage basin , representation (politics) , computer science , structural basin , environmental resource management , operations research , business , engineering , environmental science , geography , artificial intelligence , political science , geology , cartography , mathematics , politics , pure mathematics , law , paleontology
The use of Bayesian Belief Networks (BBNs) in modeling of environmental and natural resources systems has gradually grown, and they have become one of the mainstream approaches in the field. They are typically used in modeling complex systems in which policy or management decisions must be made under high uncertainties. This article documents an approach to constructing large and highly complex BBNs using a matrix representation of the model structure. This approach allows smooth construction of highly complicated models with intricate likelihood structures. A case study of the Ganges river basin, the most populated river basin of the planet, is presented. Four different development scenarios were investigated with the purpose of reaching the Millennium Development Goals and Integrated Water Resources Management goals, both promoted by the United Nations Agencies. The model results warned against the promotion of economic development policies that do not place strong emphasis on social and environmental concerns. Integr Environ Assess Manag 2012; 8: 491–502. © SETAC