Near-real time estimation of damage to buildings and infrastructure, referred to as damage nowcasting in this study, is crucial for empowering emergency responders to make informed decisions regarding evacuation orders and infrastructure repair priorities during disaster response and recovery. Here, we introduce FloodDamageCast, a machine learning framework tailored for property flood damage nowcasting. The framework leverages heterogeneous data to predict residential flood damage at a resolution of 500 meters by 500 meters within Harris County, Texas, during the 2017 Hurricane Harvey. To deal with data imbalance, FloodDamageCast incorporates a generative adversarial networks-based data augmentation coupled with an efficient machine learning model. The results demonstrate the model's ability to identify high-damage spatial areas that would be overlooked by baseline models. Insights gleaned from flood damage nowcasting can assist emergency responders to more efficiently identify repair needs, allocate resources, and streamline on-the-ground inspections, thereby saving both time and effort.

在本研究中，我们引入了FloodDamageCast这一针对财产洪水损害的机器学习框架，利用异构数据以500米×500米的分辨率预测2017年“哈维飓风”期间德克萨斯州哈里斯县的住宅洪涝损害，结果展示了该模型发现基线模型可能会忽视的高损害空间区域的能力。洪涝损害预测的见解可以帮助应急响应人员更有效地确定修复需求、分配资源，并简化地面检查，从而节约时间和精力。

FloodDamageCast: 基于机器学习和数据增强的洪水损害实时预测