Université Grenoble Alpes, CNRS, IRD, Grenoble INP, IGE, Grenoble, France.
NOAA/National Severe Storms Laboratory (NSSL), Norman, OK, USA.
Risk Anal. 2019 Jan;39(1):140-161. doi: 10.1111/risa.12921. Epub 2017 Oct 23.
This article focuses on conceptual and methodological developments allowing the integration of physical and social dynamics leading to model forecasts of circumstance-specific human losses during a flash flood. To reach this objective, a random forest classifier is applied to assess the likelihood of fatality occurrence for a given circumstance as a function of representative indicators. Here, vehicle-related circumstance is chosen as the literature indicates that most fatalities from flash flooding fall in this category. A database of flash flood events, with and without human losses from 2001 to 2011 in the United States, is supplemented with other variables describing the storm event, the spatial distribution of the sensitive characteristics of the exposed population, and built environment at the county level. The catastrophic flash floods of May 2015 in the states of Texas and Oklahoma are used as a case study to map the dynamics of the estimated probabilistic human risk on a daily scale. The results indicate the importance of time- and space-dependent human vulnerability and risk assessment for short-fuse flood events. The need for more systematic human impact data collection is also highlighted to advance impact-based predictive models for flash flood casualties using machine-learning approaches in the future.
本文侧重于概念和方法的发展，这些发展允许整合物理和社会动态，从而对特定情况下的洪水灾害中的人员损失进行模型预测。为了实现这一目标，应用随机森林分类器来评估给定情况下发生死亡的可能性，作为代表性指标的函数。在这里，选择与车辆相关的情况，因为文献表明，洪水灾害中的大多数死亡事件都属于这一类。从 2001 年到 2011 年，美国的洪水事件数据库，以及有和没有人员伤亡的数据库，都补充了其他变量，描述了风暴事件、暴露人群敏感特征的空间分布以及县级的建成环境。2015 年 5 月德克萨斯州和俄克拉荷马州的灾难性洪水被用作案例研究，以在每日尺度上绘制估计的概率人员风险的动态。结果表明，对于短期洪水事件，时间和空间相关的人类脆弱性和风险评估非常重要。还强调需要更系统地收集人类影响数据，以便将来使用机器学习方法为洪水灾害伤亡事件开发基于影响的预测模型。
