Water and Development Research Group, Aalto University, Espoo, Finland.
Integr Environ Assess Manag. 2012 Jul;8(3):491-502. doi: 10.1002/ieam.222. Epub 2011 Aug 26.
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.
贝叶斯信念网络（BBNs）在环境和自然资源系统建模中的应用逐渐增多，已成为该领域的主流方法之一。它们通常用于建模复杂系统，在这些系统中，必须在高度不确定的情况下做出政策或管理决策。本文记录了一种使用模型结构的矩阵表示来构建大型和高度复杂的 BBN 的方法。该方法允许使用复杂的似然结构平滑地构建非常复杂的模型。本文以全球人口最多的河流流域恒河流域为例，提出了一种构建大型、高度复杂的贝叶斯网络的方法。本文研究了四个不同的发展情景，目的是实现联合国机构倡导的千年发展目标和水资源综合管理目标。模型结果警告不要推行不高度重视社会和环境问题的经济发展政策。
