Jiwoo Jeong

University of Virginia

Subject Areas: Flood forecasting

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Jiwoo Jeong shared Input data and code script for comparing strategies for training LSTM models for street-scale flood prediction in Norfolk, Virginia
Type: Resource
Date: Feb. 6, 2026, 4:42 a.m.

ABSTRACT:

These are tabular input data and python code script for LSTM surrogate model built for real-time street flood prediction in Norfolk, VA. The LSTM surrogate model approximates water depth on streets generated by TUFLOW. The inputs of the model are topographic feature: elevation, and environmental features such as hourly rainfall, cumulative rainfall in previous hours, hourly tide level, etc. The output of the model is hourly water depth on streets during storm events generated by the TUFLOW model.

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Input data and code script for comparing strategies for training LSTM models for street-scale flood prediction in Norfolk, Virginia
Created: Feb. 6, 2026, 4:42 a.m.
Authors: Jeong, Jiwoo

ABSTRACT:

These are tabular input data and python code script for LSTM surrogate model built for real-time street flood prediction in Norfolk, VA. The LSTM surrogate model approximates water depth on streets generated by TUFLOW. The inputs of the model are topographic feature: elevation, and environmental features such as hourly rainfall, cumulative rainfall in previous hours, hourly tide level, etc. The output of the model is hourly water depth on streets during storm events generated by the TUFLOW model.

Show More