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首页> 外文期刊>Journal of Environmental Management >Application of statistical techniques to proportional loss data: Evaluating the predictive accuracy of physical vulnerability to hazardous hydro-meteorological events
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Application of statistical techniques to proportional loss data: Evaluating the predictive accuracy of physical vulnerability to hazardous hydro-meteorological events

机译：统计技术在比例损失数据中的应用：评估物理危险性对危险水文气象事件的预测准确性

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摘要

Knowledge about the cause of differential structural damages following the occurrence of hazardous hydro-meteorological events can inform more effective risk management and spatial planning solutions. While studies have been previously conducted to describe relationships between physical vulnerability and features about building properties, the immediate environment and event intensity proxies, several key challenges remain In particular, observations, especially those associated with high magnitude events, and studies designed to evaluate a comprehensive range of predictive features are both limited. To build upon previous developments, we described a workflow to support the continued development and assessment of empirical, multivariate physical vulnerability functions based on predictive accuracy. Within this workflow, we evaluated several statistical approaches, namely generalized linear models and their more complex alternatives. A series of models were built 1) to explicitly consider the effects of dimension reduction, 2) to evaluate the inclusion of interaction effects between and among predictors, 3) to evaluate an ensemble prediction method for applications where data observations are sparse, 4) to describe how model results can inform about the relative importance of predictors to explain variance in expected damages and 5) to assess the predictive accuracy of the models based on prescribed metrics. The utility of the workflow was demonstrated on data with characteristics of what is commonly acquired in ex-post field assessments. The workflow and recommendations from this study aim to provide guidance to researchers and practitioners in the natural hazards community.
机译：有关危险的水文气象事件发生后结构差异性损坏原因的知识可以为更有效的风险管理和空间规划解决方案提供信息。尽管先前曾进行过研究来描述物理脆弱性与建筑物特性，直接环境和事件强度代理之间的关系，但仍存在一些关键挑战，尤其是观察，尤其是与高强度事件相关的观察，以及旨在评估综合性的研究。预测特征的范围都有限。为了建立在先前的开发基础之上，我们描述了一种工作流，以支持基于预测准确性对经验，多变量物理漏洞功能的持续开发和评估。在此工作流程中，我们评估了几种统计方法，即广义线性模型及其更复杂的替代方法。建立了一系列模型：1）明确考虑降维的影响； 2）评估预测变量之间的相互作用效应； 3）评估适用于数据观测稀疏的应用的整体预测方法； 4）描述模型结果如何告知预测因素以解释预期损害的方差的相对重要性，以及5）根据规定的指标评估模型的预测准确性。工作流的效用在具有事后现场评估通常获得的特征的数据上得到了证明。这项研究的工作流程和建议旨在为自然灾害界的研究人员和从业人员提供指导。

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