This resource contains the Jupyter Notebook for a surrogate model (SM) designed to improve the accuracy of conceptual flood inundation mapping (FIM) by mimicking the behavior of a high-fidelity hydrodynamic model.
The study leverages the National Oceanic and Atmospheric Administration (NOAA) Office of Water Prediction's (OWP) Height Above Nearest Drainage-Flood Inundation Mapping (HAND-FIM) method as the baseline framework, which enables large-scale flood mapping at low computational costs.
The surrogate model (SM) employs machine learning techniques, specifically a Random Forest algorithm, to replicate critical hydrodynamic characteristics derived from a 2D Hydrologic Engineering Center-River Analysis System (HEC-RAS) model, a high-fidelity representation of flood dynamics. This approach enhances the fidelity of the simpler HAND-FIM by infusing it with insights from the more detailed HEC-RAS model. The model development is applied across 14 carefully selected sub-watersheds in the Amite River Basin.

Key features of the resource include:

- Surrogate Model: Demo code built using the Random Forest algorithm to predict flood extents based on various input features.
- Input Features: These include the HAND-FIM generated for a historic flood event in August 2016 using the National Water Model (NWM) streamflow and the Office of Water Predictions (OWP) HAND-FIM Synthetic Rating Curves (SRC). Other inputs, such as the Digital Elevation Model (DEM), slope, aspect, land use/land cover (LULC) data, impervious surface data, and river network information (RiverNet).
- Target Data: High-resolution flood extent data from the HEC-RAS model (HEC-RAS-FIM), serving as the ground truth for training the surrogate model.

Contribution:
This work demonstrates how a data-driven, machine-learning approach can bridge the gap between conceptual flood models and hydrodynamic models, improving the accuracy and reliability of large-scale flood mapping while maintaining computational efficiency. The surrogate model mimics complex relationships between terrain, hydrology, and flood extents, making it a powerful tool for flood prediction in regions where detailed hydrodynamic modeling may be too costly or time-consuming.

This research was conducted as part of the NOAA National Water Center Innovators Program, Summer Institute 2023.