Predicting Flash Flood Economic Damage at the Community Scale: Empirical Zero-Inflated Model with Semicontinuous Data

Flash floods (FFs) are a leading cause of natural hazard‐related fatalities in the US, posing unique challenges due to their localized impact and rapid onset. Traditional FF susceptibility assessments often fail to account for regional variations. Addressing this, we introduce Dynamic Flash Flood Susceptibility (DFFS), a GIS‐based solution designed for dynamic, region‐specific FF assessment. DFFS operates through four key steps: extracting FF data from the NOAA Storm Events Database for census tracts (CTs) in any region of interest, conducting spatial hotspot analysis to identify areas of high and low FF occurrences, applying causal discovery to identify region‐specific causal factors (from potential factors such as geology, terrain, and meteorology), and using machine learning to calculate susceptibility scores, resulting in a detailed FF susceptibility map. Our case studies in three Texas regions—Dallas‐Fort Worth, Greater Austin, and Greater Houston—revealed distinct causal relationships, with factors like storm duration consistently influential across all regions, while others, such as population density specific to Greater Austin. Furthermore, DFFS demonstrated high accuracy (0.87, 0.86, 0.94) and F1‐scores (0.88, 0.86, 0.96) in computing community susceptibility scores for these regions. We demonstrate DFFS's tangible value in FF risk management and policy‐making, providing a data‐driven and generalizable tool for FF assessment.

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Predicting Flash Flood Economic Damage at the Community Scale: Empirical Zero-Inflated Model with Semicontinuous Data

Cited by 2 publications

References 45 publications

FF-BERT: A BERT-based ensemble for automated classification of web-based text on flash flood events

FF-BERT: A BERT-based ensemble for automated classification of web-based text on flash flood events

A GIS‐based tool for dynamic assessment of community susceptibility to flash flooding

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