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Building Bayesian Networks for Problems of Risk Attributable to Natural Hazards

机译：建立贝叶斯网络以解决自然灾害引起的风险问题

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摘要

Building Bayesian networks (BNs) for problems of risk attributable to natural hazards is a complex problem. Risk analysis makes use of domain experts' knowledge, which is an essential element of input for the analysis. This knowledge is acquired from fragmented sources of expertise. The effectiveness of the analysis depends on the integration of the knowledge and data, which is of vital significance to decision makers. Setting up the flow of information for the acquired knowledge, which is from different fragmented sources, is a difficult task. Moreover, the process of dealing with the fragmented knowledge of domain experts may be equally complicated because of the large number of variables involved in the case of problems of risk attributable to natural hazards. To this end, a new approach using graph-theoretic techniques is proposed in this paper for integrating experts' knowledge and data for setting up the flow of information for building BNs, so that it can be used in risk analysis. To demonstrate the approach, a case study on windstorm-induced damage of a roof structure is considered.
机译：建立贝叶斯网络（BN）来解决自然灾害引起的风险问题是一个复杂的问题。风险分析利用领域专家的知识，这是分析输入的基本要素。这些知识是从零散的专业知识来源中获得的。分析的有效性取决于知识和数据的整合，这对决策者至关重要。建立来自不同零碎资源的获取知识的信息流是一项艰巨的任务。此外，处理领域专家知识零散的过程可能同样复杂，因为在自然灾害引起的风险问题中涉及大量变量。为此，本文提出了一种使用图论技术的新方法，该方法用于整合专家的知识和数据，以建立用于构建BN的信息流，从而可以将其用于风险分析。为了演示该方法，考虑了一个由暴风雨引起的屋顶结构损坏的案例研究。

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