Flood loss estimation models are developed using synthetic or empirical approaches. The synthetic approach consists of what-if scenarios developed by experts. The empirical models are based on statistical analysis of empirical loss data. In this study, we propose a novel Bayesian Data-Driven approach to enhance established synthetic models using available empirical data from recorded events. For five case studies in Western Europe, the resulting Bayesian Data-Driven Synthetic (BDDS) model enhances synthetic model predictions by reducing the prediction errors and quantifying the uncertainty and reliability of loss predictions for post-event scenarios and future events. The performance of the BDDS model for a potential future event is improved by integration of empirical data once a new flood event affects the region. The BDDS model, therefore, has high potential for combining established synthetic models with local empirical loss data to provide accurate and reliable flood loss predictions for quantifying future risk.

洪水损失估算模型是使用综合或经验方法开发的。综合方法由专家开发的假设情景组成。经验模型基于经验损失数据的统计分析。在这项研究中，我们提出了一种新颖的贝叶斯数据驱动方法，以使用已记录事件的可用经验数据来增强已建立的综合模型。对于西欧的五个案例研究，生成的贝叶斯数据驱动的合成（BDDS）模型通过减少预测误差并量化事后场景和未来事件的损失预测的不确定性和可靠性，增强了综合模型的预测。一旦新的洪灾事件影响了该地区，就可以通过整合经验数据来提高BDDS模型对潜在未来事件的性能。因此，BDDS模型