Models for the predictions of monetary losses from floods mainly blend data deemed to represent a single flood type and region. Moreover, these approaches largely ignore indicators of preparedness and how predictors may vary between regions and events, challenging the transferability of flood loss models. We use a flood loss database of 1812 German flood-affected households to explore how Bayesian multilevel models can estimate normalised flood damage stratified by event, region, or flood process type. Multilevel models acknowledge natural groups in the data and allow each group to learn from others. We obtain posterior estimates that differ between flood types, with credibly varying influences of water depth, contamination, duration, implementation of property-level precautionary measures, insurance, and previous flood experience; these influences overlap across most events or regions, however. We infer that the underlying damaging processes of distinct flood types deserve further attention. Each reported flood loss and affected region involved mixed flood types, likely explaining the uncertainty in the coefficients. Our results emphasise the need to consider flood types as an important step towards applying flood loss models elsewhere. We argue that failing to do so may unduly generalise the model and systematically bias loss estimations from empirical data.

中文翻译：

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贝叶斯多级模型估算的居民洪水损失

洪水造成的货币损失的预测模型主要是将被认为代表单一洪水类型和地区的数据混合在一起。此外，这些方法很大程度上忽略了备灾指标以及区域和事件之间的预测因素如何变化，从而对洪水损失模型的可传递性提出了挑战。我们使用1812个德国受洪水影响的家庭的洪水损失数据库来探索贝叶斯多级模型如何估计按事件，地区或洪水过程类型分层的标准化洪水损害。多级模型可以确认数据中的自然组，并允许每个组向其他人学习。我们得出的后验估计在洪水类型之间是不同的，水深，污染，持续时间，财产级别的预防措施的实施，保险和以前的洪水经验的影响确实发生了变化。但是，这些影响在大多数事件或地区上都是重叠的。我们推断，不同洪水类型的潜在破坏过程值得进一步关注。每个报告的洪水损失和受影响地区都涉及混合洪水类型，这可能解释了系数的不确定性。我们的结果强调需要将洪水类型视为在其他地方应用洪水损失模型的重要步骤。我们认为，如果不这样做，可能会过分泛化模型并从经验数据中系统性地造成损失估计。我们的结果强调需要将洪水类型视为在其他地方应用洪水损失模型的重要步骤。我们认为，如果不这样做，可能会过分泛化模型并从经验数据中系统性地造成损失估计。我们的结果强调需要将洪水类型视为在其他地方应用洪水损失模型的重要步骤。我们认为，如果不这样做，可能会过分泛化模型并从经验数据中系统性地造成损失估计。