Hybrid Surrogate Model for Timely Prediction of Flash Flood Inundation Maps Caused by Rapid River Overflow

André D. L. Zanchetta, Paulin Coulibaly

Forecasting 2022 article cited by 38

Source Open-access PDF More in Climate & Environment

importance 7/10 partially solved Flood Risk Assessment and Management flood forecasting stage: research

Problem

Physically based hydraulic models are too computationally slow to generate flash flood inundation maps in real time for urban early warning systems.

Why it matters

Urban flash floods threaten lives, and timely inundation forecasts enable effective early warnings.

What the AI did

Built a hybrid machine learning surrogate of a quasi-2D PCSWMM urban flood model for a Toronto catchment, evaluated with error, bias, correlation, and contingency metrics.

Results

Surrogate rapidly generated future flood inundation maps comparable to physically based model simulations; numeric metrics not stated in source.

Impact

Demonstrates feasibility of fast surrogate forecasting, validated on a single urban catchment.

Record

Venue	Forecasting
Published	2022
Work type	article
Authors	André D. L. Zanchetta, Paulin Coulibaly
Citations	38 (OpenAlex, at import time)
References	41
Model or method	not stated in source
Organization	not stated in source
Primary source	https://doi.org/10.3390/forecast4010007
Open access	gold
Keywords	machine learning, surrogate model, flash flood, inundation mapping, early warning

Importance rating

7 / 10

significant — thousands to millions affected

Urban flash floods threaten lives and real-time inundation forecasts are called paramount for early warning; no scale quantified but urban flood warning plausibly affects many people.

Basis: inferred. Judged from the source text, which gives no explicit scale. Stated scale: not stated in source. Method and limits.

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