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Bayesian network learning for natural hazard analyses

机译：贝叶斯网络学习用于自然灾害分析

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Modern natural hazards research requires dealing with several uncertainties that arise from limited process knowledge, measurement errors, censored and incomplete observations, and the intrinsic randomness of the governing processes. Nevertheless, deterministic analyses are still widely used in quantitative hazard assessments despite the pitfall of misestimating the hazard and any ensuing risks. In this paper we show that Bayesian networks offer a flexible framework for capturing and expressing a broad range of uncertainties encountered in natural hazard assessments. Although Bayesian networks are well studied in theory, their application to real-world data is far from straightforward, and requires specific tailoring and adaptation of existing algorithms. We offer suggestions as how to tackle frequently arising problems in this context and mainly concentrate on the handling of continuous variables, incomplete data sets, and the interaction of both. By way of three case studies from earthquake, flood, and landslide research, we demonstrate the method of data-driven Bayesian network learning, and showcase the flexibility, applicability, and benefits of this approach. Our results offer fresh and partly counterintuitive insights into well-studied multivariate problems of earthquakeinduced ground motion prediction, accurate flood damage quantification, and spatially explicit landslide prediction at the regional scale. In particular, we highlight how Bayesian networks help to express information flow and independence assumptions between candidate predictors. Such knowledge is pivotal in providing scientists and decision makers with well-informed strategies for selecting adequate predictor variables for quantitative natural hazard assessments.
机译：现代自然灾害研究要求处理由于过程知识有限，测量误差，检查结果不完整和检查不完整以及控制过程的固有随机性而引起的几种不确定性。尽管如此，确定性分析仍被广泛用于定量危害评估中，尽管存在错误估计危害和随之而来的风险的陷阱。在本文中，我们表明贝叶斯网络为捕获和表达自然灾害评估中遇到的各种不确定性提供了灵活的框架。尽管在理论上对贝叶斯网络进行了很好的研究，但将其应用于现实世界数据远非简单易行，需要对现有算法进行特定的调整和调整。我们提供一些建议，例如在这种情况下如何解决经常出现的问题，主要集中在处理连续变量，不完整的数据集以及两者之间的相互作用上。通过地震，洪水和滑坡研究的三个案例研究，我们演示了数据驱动的贝叶斯网络学习方法，并展示了这种方法的灵活性，适用性和优势。我们的研究结果提供了对地震诱发的地震动预测，洪水泛滥的准确量化以及区域范围内空间上明确的滑坡预测等经过深入研究的多元问题的新鲜且部分与直觉相反的见解。特别是，我们重点介绍了贝叶斯网络如何帮助表达候选预测变量之间的信息流和独立性假设。这些知识对于为科学家和决策者提供明智的策略，以选择适当的预测变量进行定量自然灾害评估至关重要。

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