Flood risk modelling aims to quantify the probability of flooding and the resulting consequences for exposed elements. The assessment of flood damage is a core task that requires the description of complex flood damage processes including the influences of flooding intensity and vulnerability characteristics. Multi-variable modelling approaches are better suited for this purpose than simple stage–damage functions. However, multi-variable flood vulnerability models require detailed input data and often have problems in predicting damage for regions other than those for which they have been developed. A transfer of vulnerability models usually results in a drop of model predictive performance. Here we investigate the questions as to whether data from the open-data source OpenStreetMap is suitable to model flood vulnerability of residential buildings and whether the underlying standardized data model is helpful for transferring models across regions. We develop a new data set by calculating numerical spatial measures for residential-building footprints and combining these variables with an empirical data set of observed flood damage. From this data set random forest regression models are learned using regional subsets and are tested for predicting flood damage in other regions. This regional split-sample validation approach reveals that the predictive performance of models based on OpenStreetMap building geometry data is comparable to alternative multi-variable models, which use comprehensive and detailed information about preparedness, socio-economic status and other aspects of residential-building vulnerability. The transfer of these models for application in other regions should include a test of model performance using independent local flood data. Including numerical spatial measures based on OpenStreetMap building footprints reduces model prediction errors (MAE – mean absolute error – by 20 % and MSE – mean squared error – by 25 %) and increases the reliability of model predictions by a factor of 1.4 in terms of the hit rate when compared to a model that uses only water depth as a predictor. This applies also when the models are transferred to other regions which have not been used for model learning. Further, our results show that using numerical spatial measures derived from OpenStreetMap building footprints does not resolve all problems of model transfer. Still, we conclude that these variables are useful proxies for flood vulnerability modelling because these data are consistent (i.e. input variables and underlying data model have the same definition, format, units, etc.) and openly accessible and thus make it easier and more cost-effective to transfer vulnerability models to other regions.

中文翻译：

---

OpenStreetMap建筑数据对洪水漏洞建模有用吗？

洪水风险建模旨在量化洪水的可能性及其对裸露元素造成的后果。洪水破坏的评估是一项核心任务，需要描述复杂的洪水破坏过程，包括洪水强度和脆弱性特征的影响。多变量建模方法比简单的阶段破坏函数更适合于此目的。但是，多变量洪水脆弱性模型需要详细的输入数据，并且在预测针对其开发区域以外的区域的破坏时通常会遇到问题。漏洞模型的转移通常会导致模型的预测性能下降。在这里，我们调查以下问题：来自开放数据源OpenStreetMap的数据是否适合对住宅建筑物的洪水脆弱性进行建模，以及基础的标准化数据模型是否有助于跨区域传输模型。我们通过计算住宅建筑占地面积的数值空间度量并将这些变量与观测到的洪灾破坏经验数据集相结合，从而开发出新的数据集。从该数据集中，使用区域子集学习随机森林回归模型，并对其进行测试以预测其他区域的洪灾破坏。这种区域划分样本验证方法表明，基于OpenStreetMap建筑几何数据的模型的预测性能可与替代性多变量模型相媲美，后者使用了关于准备情况的全面详细信息，社会经济地位和住宅建筑脆弱性的其他方面。这些模型在其他地区的应用转移应包括使用独立的本地洪水数据进行的模型性能测试。包括基于OpenStreetMap建筑占地面积的数值空间测量，可以减少模型预测误差（MAE –平均绝对误差– 20％，MSE –均方误差– 25％），并且将模型预测的可靠性提高1.4倍。与仅使用水深作为预测指标的模型相比时的命中率。当模型转移到尚未用于模型学习的其他区域时，这也适用。此外，我们的结果表明，使用从OpenStreetMap建筑足迹中导出的数值空间度量不能解决模型转移的所有问题。仍然，