Eawag, Swiss Federal Institute of Aquatic Science and Technology, Dübendorf, Switzerland.
Integr Environ Assess Manag. 2012 Jul;8(3):462-72. doi: 10.1002/ieam.233. Epub 2011 Aug 24.
As rehabilitation of previously channelized rivers becomes more common worldwide, flexible integrative modeling tools are needed to help predict the morphological, hydraulic, economic, and ecological consequences of the rehabilitation activities. Such predictions can provide the basis for planning and long-term management efforts that attempt to balance the diverse interests of river system stakeholders. We have previously reported on a variety of modeling methods and decision support concepts that can assist with various aspects of the river rehabilitation process. Here, we bring all of these tools together into a probability network model that links management actions, through morphological and hydraulic changes, to the ultimate ecological and economic consequences. Although our model uses a causal graph representation common to Bayesian networks, we do not limit ourselves to discrete-valued nodes or conditional Gaussian distributions as required by most Bayesian network implementations. This precludes us from carrying out easy probabilistic inference but gives us the advantages of functional and distributional flexibility and enhanced predictive accuracy, which we believe to be more important in most environmental management applications. We exemplify model application to a large, recently completed rehabilitation project in Switzerland.
随着全球范围内对先前渠化河流的修复变得越来越普遍，需要灵活的综合建模工具来帮助预测修复活动对河流形态、水力、经济和生态的影响。这些预测可以为规划和长期管理工作提供依据，努力平衡河流系统利益相关者的各种利益。我们之前曾报告过各种建模方法和决策支持概念，这些方法和概念可以协助河流修复过程的各个方面。在这里，我们将所有这些工具整合到一个概率网络模型中，该模型通过形态和水力变化将管理措施与最终的生态和经济后果联系起来。虽然我们的模型使用了贝叶斯网络中常见的因果图表示法，但我们并不像大多数贝叶斯网络实现所要求的那样，将自己局限于离散值节点或条件高斯分布。这使我们无法进行简单的概率推断，但使我们具有功能和分布灵活性以及增强的预测准确性方面的优势，我们认为这些优势在大多数环境管理应用中更为重要。我们举例说明了该模型在瑞士最近完成的一个大型修复项目中的应用。
