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Predicting peak inundation depths with a physics informed machine learning model.

Cheng-Chun Lee¹, Lipai Huang², Federico Antolini³

¹Urban Resilience.AI Lab, Zachry Department of Civil and Environmental Engineering, Texas A&M University, College Station, TX, USA.

Scientific Reports

|June 27, 2024

Summary

This summary is machine-generated.

A new machine learning model, MaxFloodCast, accurately predicts flood inundation depths using hydrodynamic simulations. This tool enhances emergency response and flood risk management with efficient, interpretable forecasts.

Keywords:

Flood depth forecast Interpretable model Machine learning Near-time prediction

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Area of Science:

Environmental Science
Hydrology
Machine Learning

Background:

Accurate flood inundation information is critical for effective disaster management and infrastructure operation.
Existing methods for flood prediction can be computationally intensive and lack interpretability.
The need for rapid, reliable flood forecasting is paramount during extreme weather events.

Purpose of the Study:

To develop and validate an efficient and interpretable machine learning model for predicting flood inundation depths.
To assess the model's performance using physics-based hydrodynamic simulations and real-world flood events.
To demonstrate the model's utility in supporting near-time floodplain management and emergency operations.

Main Methods:

Developed MaxFloodCast, a machine learning model trained on physics-based hydrodynamic simulations.
Utilized data from Harris County, Texas for model training and validation.
Evaluated model performance using metrics such as average and Root Mean Square Error (RMSE) on unseen data.
Validated the model against historical flood events, including Hurricane Harvey and Tropical Storm Imelda.

Main Results:

MaxFloodCast achieved a high average of 0.949 and an RMSE of 0.61 ft (0.19 m) on unseen data.
The model demonstrated reliable forecasting of peak flood inundation depths.
Validated performance against Hurricane Harvey and Tropical Storm Imelda confirmed its practical applicability.
The model significantly reduced computational time compared to traditional methods.

Conclusions:

MaxFloodCast provides accurate and interpretable flood inundation depth predictions, enhancing flood risk management.
The model's efficiency and interpretability support critical decision-making for emergency response and flood mitigation strategies.
MaxFloodCast has the potential to improve near-time floodplain management and prioritize areas with critical infrastructure during flood events.