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Decision-tree analysis of factors influencing rainfall-related building structure and content damage

机译：降雨相关建筑结构和内容破坏影响因素的决策树分析

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Flood-damage prediction models are essential building blocks in flood risk assessments. So far, little research has been dedicated to damage from small-scale urban floods caused by heavy rainfall, while there is a need for reliable damage models for this flood type among insurers and water authorities. The aim of this paper is to investigate a wide range of damage-influencing factors and their relationships with rainfall-related damage, using decision-tree analysis. For this, district-aggregated claim data from private property insurance companies in the Netherlands were analysed, for the period 1998-2011. The databases include claims of waterrelated damage (for example, damages related to rainwater intrusion through roofs and pluvial flood water entering buildings at ground floor). Response variables being modelled are average claim size and claim frequency, per district, per day. The set of predictors include rainfall-related variables derived from weather radar images, topographic variables from a digital terrain model, building-related variables and socioeconomic indicators of households. Analyses were made separately for property and content damage claim data. Results of decision-tree analysis show that claim frequency is most strongly associated with maximum hourly rainfall intensity, followed by real estate value, ground floor area, household income, season (property data only), buildings age (property data only), a fraction of homeowners (content data only), a and fraction of low-rise buildings (content data only). It was not possible to develop statistically acceptable trees for average claim size. It is recommended to investigate explanations for the failure to derive models. These require the inclusion of other explanatory factors that were not used in the present study, an investigation of the variability in average claim size at different spatial scales, and the collection of more detailed insurance data that allows one to distinguish between the effects of various damage mechanisms to claim size. Cross-validation results show that decision trees were able to predict 22-26% of variance in claim frequency, which is considerably better compared to results from global multiple regression models (11-18% of variance explained). Still, a large part of the variance in claim frequency is left unexplained, which is likely to be caused by variations in data at subdistrict scale and missing explanatory variables.
机译：洪水破坏预测模型是洪水风险评估中必不可少的组成部分。到目前为止，很少有研究致力于降雨造成的小规模城市洪水造成的破坏，而保险公司和水务部门则需要针对这种洪水类型的可靠破坏模型。本文的目的是使用决策树分析来研究各种损害影响因素及其与降雨相关损害的关系。为此，分析了1998-2011年期间荷兰私人财产保险公司的地区汇总索赔数据。该数据库包括与水有关的损害的索赔（例如，与雨水通过屋顶侵入以及进入地下建筑物的雨洪有关的损害）。建模的响应变量是每个区域每天的平均索赔大小和索赔频率。一组预测变量包括与天气雷达图像有关的与降雨相关的变量，来自数字地形模型的地形变量，与建筑相关的变量以及家庭的社会经济指标。分别对财产和财产损失索赔数据进行了分析。决策树分析结果显示，索赔频率与每小时最大降雨强度密切相关，其次是房地产价值，地面面积，家庭收入，季节（仅财产数据），建筑物年龄（仅财产数据），一小部分房主的数量（仅内容数据），低层建筑物的比例（仅内容数据）。不可能为平均索赔额制定统计上可接受的树。建议调查有关无法导出模型的解释。这些要求包括本研究中未使用的其他解释性因素，调查不同空间范围内的平均理赔大小的可变性，以及收集更详细的保险数据，从而可以区分各种损害的影响。声明大小的机制。交叉验证的结果表明，决策树能够预测索赔频率的22％至26％的差异，这与全局多元回归模型的结果（解释了11％至18％的差异）相比要好得多。尽管如此，索赔频率的很大一部分差异仍然无法解释，这很可能是由分区规模的数据变化和缺少解释变量引起的。

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