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Risk Anal. 2019 Jan;39(1):140-161. doi: 10.1111/risa.12921. Epub 2017 Oct 23.

Toward Probabilistic Prediction of Flash Flood Human Impacts.

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Université Grenoble Alpes, CNRS, IRD, Grenoble INP, IGE, Grenoble, France.
NOAA/National Severe Storms Laboratory (NSSL), Norman, OK, USA.
NOAA/National Severe Storms Laboratory, and Atmospheric Radar Research Center, University of Oklahoma, Norman, OK, USA.
Cooperative Institute for Mesoscale Meteorological Studies, University of Oklahoma, Norman, OK, USA.


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.


Dynamic risk mapping; machine-learning predictions; probability of flash flood casualty

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