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Toward Probabilistic Prediction of Flash Flood Human Impacts
Galateia Terti1, Isabelle Ruin1, Jonathan J Gourley2
1Université Grenoble Alpes, CNRS, IRD, Grenoble INP, IGE, Grenoble, France.
Summary
This study models flash flood fatalities using machine learning, focusing on vehicle-related incidents. It highlights the need for better data to improve future risk assessments for human losses.
Area of Science:
- Hydrology and Disaster Risk Reduction
- Computational Social Science
- Machine Learning Applications in Environmental Science
Background:
- Flash floods pose significant risks, causing substantial human losses, particularly in vehicle-related incidents.
- Existing models often lack the integration of dynamic social and physical factors crucial for accurate fatality prediction.
- Understanding human vulnerability and risk requires considering time- and space-dependent variables.
Purpose of the Study:
- To develop and apply a machine learning model for forecasting circumstance-specific human losses during flash floods.
- To integrate physical and social dynamics for a more accurate assessment of flash flood-related fatalities.
- To evaluate the model's performance using a case study of the 2015 Texas and Oklahoma flash floods.
Main Methods:
- Utilized a random forest classifier to predict the likelihood of fatality occurrence based on key indicators.
- Developed a comprehensive database of flash flood events (2001-2011, US) incorporating storm, population, and built environment data.
- Applied the model to a case study of the May 2015 Texas and Oklahoma flash floods to map daily probabilistic human risk.
Main Results:
- The study successfully modeled the probabilistic human risk associated with flash floods, emphasizing vehicle-related circumstances.
- Results underscore the critical importance of time- and space-dependent factors in assessing human vulnerability and risk for short-fuse flood events.
- The analysis demonstrated the potential of machine learning approaches in predicting flash flood casualties.
Conclusions:
- Accurate forecasting of flash flood human losses necessitates the integration of dynamic physical and social variables.
- Systematic collection of human impact data is essential for advancing predictive models in flood risk assessment.
- Machine learning offers a promising avenue for developing more sophisticated, impact-based predictive models for flash flood casualties.
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