德克萨斯州中部军事训练用地的水质和自然资源管理:通过贝叶斯网络改进的决策支持

William E. Fox , Zenon Medina-Cetina , Jay Angerer , Patricia Varela , Ji Ryang Chung
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引用次数: 8

摘要

长期以来,军事训练用地的保护和管理一直围绕着维持一支可行的战斗部队的独特标准而发展。代替这一主要任务,景观往往经历加速退化和结构和功能能力的丧失,以提供所需的生态系统服务(包括基本训练任务)。为了帮助军事土地管理者以及任何制定和实施自然资源管理决策的人;我们支持一种创新的方法,通过使用贝叶斯网络,将证据和诊断与预测应用于决策过程。下面是一个在决策支持过程中使用贝叶斯网络的例子。我们利用胡德堡军事基地的数据和经验建立了最初的网络;然后在传播节点关系时整合作者和工程师的专家意见。通过这种方法,我们展示了军事土地管理者如何将不同的证据流(包括经验、模型生成或专家意见)整合到一个决策支持网络中。下面的例子是基于美国陆军内部的土地管理问题开发的,但在某种形式的土地管理下,该过程可以在大多数生态系统中进行调整和实施。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Water Quality & natural resource management on military training lands in Central Texas: Improved decision support via Bayesian Networks

The conservation and management of military training lands has long evolved around the unique criteria for maintaining a viable fighting force. In lieu of this primary mission, landscapes have often experienced accelerated degradation and loss of structural and functional capabilities for providing desired ecosystem services (including the basic training mission). In an effort to aid military land managers as well as anyone who makes and implements decisions for natural resource management; we support an innovative approach towards the integration of evidence and the application of diagnosis and prognosis for the decision-making process through the use of Bayesian Networks. Illustrated below is an example for utilizing Bayesian Networks in the decision support process. We utilized data and experience from ongoing efforts at the Fort Hood Military Installation to build the initial network; then integrate expert input from authors and engineers in propagating the node relationships. Through this approach, we demonstrate how military land managers can integrate varying streams of evidence, including empirical, model generated or expert opinion, into a network for decision support. The example below is developed based upon land management issues within the U.S. Army, but the process can be adapted and implemented across most all ecosystems under some form of land management.

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