Adel et al. (2026) Nationwide Prediction of Flood Damage Costs in the Contiguous United States Using ML-Based Models: A Data-Driven Approach

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Identification

• Journal: Hydrology
• Year: 2026
• Date: 2026-01-14
• Authors: Khaled M. Adel, Hany G. Radwan, Mohamed M. Morsy
• DOI: 10.3390/hydrology13010031

Research Groups

Not explicitly stated in the provided text.

Short Summary

This study develops a data-driven framework to estimate flood damage costs across the contiguous United States using a comprehensive database of 17,407 flood events. The optimal hybrid regression–classification framework achieved high predictive accuracy, demonstrating the potential for enhanced nationwide, event-based flood-damage cost assessment.

Objective

• To develop a data-driven framework for estimating flood damage costs across the contiguous United States.

Study Configuration

• Spatial Scale: Contiguous United States, nationwide.
• Temporal Scale: Event-based, compiling data from 17,407 flood events.

Methodology and Data

• Models used: Optimal hybrid regression–classification framework.
• Data sources: National Oceanic and Atmospheric Administration (NOAA), National Water Model (NWM), United States Geological Survey (USGS NED), U.S. Census Bureau, incorporating approximately 38 parameters related to hydrologic, climatic, and socioeconomic factors.

Main Results

• The optimal hybrid regression–classification framework achieved high correlation coefficients: 0.97 (training), 0.77 (testing), and 0.81 (validation).
• The framework exhibited minimal bias: −5.85 USD (training), −107.8 USD (testing), and −274.5 USD (validation).
• The findings demonstrate the potential of nationwide, event-based predictive approaches to enhance flood-damage cost assessment.

Contributions

• Development of a novel data-driven framework for estimating flood damage costs across the entire contiguous United States.
• Creation of a comprehensive database of 17,407 flood events integrating diverse hydrologic, climatic, and socioeconomic parameters.
• Introduction of an optimal hybrid regression–classification framework that provides a practical tool for nationwide flood risk evaluation and resource planning.

Funding

Not explicitly stated in the provided text.

Citation

@article{Adel2026Nationwide,
  author = {Adel, Khaled M. and Radwan, Hany G. and Morsy, Mohamed M.},
  title = {Nationwide Prediction of Flood Damage Costs in the Contiguous United States Using ML-Based Models: A Data-Driven Approach},
  journal = {Hydrology},
  year = {2026},
  doi = {10.3390/hydrology13010031},
  url = {https://doi.org/10.3390/hydrology13010031}
}