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Testing empirical and synthetic flood damage models: the case of Italy

机译：测试实证和合成洪水损坏模型：意大利的情况

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Flood risk management generally relies on economic assessments performed by using flood loss models of different complexity, ranging from simple univariable models to more complex multivariable models. The latter account for a large number of hazard, exposure and vulnerability factors, being potentially more robust when extensive input information is available. We collected a comprehensive data set related to three recent major flood events in northern Italy (Adda 2002, Bacchiglione 2010 and Secchia 2014), including flood hazard features (depth, velocity and duration), building characteristics (size, type, quality, economic value) and reported losses. The objective of this study is to compare the performances of expert-based and empirical (both uni-and multivariable) damage models for estimating the potential economic costs of flood events to residential buildings. The performances of four literature flood damage models of different natures and complexities are compared with those of univariable, bivariable and multivariable models trained and tested by using empirical records from Italy. The uni- and bivariable models are developed by using linear, logarithmic and square root regression, whereas multivariable models are based on two machine-learning techniques: random forest and artificial neural networks. Results provide important insights about the choice of the damage modelling approach for operational disaster risk management. Our findings suggest that multivariable models have better potential for producing reliable damage estimates when extensive ancillary data for flood event characterisation are available, while univariable models can be adequate if data are scarce. The analysis also highlights that expert-based synthetic models are likely better suited for transferability to other areas compared to empirically based flood damage models.
机译：洪水风险管理一般依赖于使用不同复杂性的洪水损失模型进行的经济评估，从简单的单变种模型到更复杂的多变量型号。后者占大量危险，曝光和漏洞因素，当广泛的输入信息可用时可能更加强劲。我们收集了一个与意大利北部三个主要洪水事件相关的全面数据集（ADDA 2002，Bacchiglione 2010和Secchia 2014），包括洪水危险功能（深度，速度和持续时间），建筑特性（尺寸，类型，质量，经济价值）报告的损失。本研究的目的是比较专家的和经验（UNI和多变量）损坏模型的性能，以估算洪水事件的潜在经济成本到住宅建筑。将不同自然和复杂性的四种文学洪水损伤模型的性能与使用意大利的经验记录进行培训和测试的单一性，可自变和多变量的型号。通过使用线性，对数和平方根回归开发了单一和可行的模型，而多变量型号基于两种机器学习技术：随机林和人工神经网络。结果对操作灾害风险管理损伤建模方法的选择提供了重要的见解。我们的研究结果表明，当可用的洪水事件表征广泛的辅助数据时，多变量的型号具有更好的产生可靠的损坏估算，而如果数据稀缺，则不可变化的型号可能是足够的。与经验基础的洪水损伤模型相比，分析还突出了基于专家的合成模型可能更适合与其他区域的可转换性。

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