Elsadek et al. (2026) Improving Flood Susceptibility Modeling Through a Multi-Criteria Feature Selection Framework

⚠️ Warning: This summary was generated from the abstract only, as the full text was not available.

Identification

• Journal: Water
• Year: 2026
• Date: 2026-09-28
• Authors: Wael M. Elsadek, Shinjiro Kanae
• DOI: 10.3390/w18192411

Research Groups

• Department of Civil Engineering, University of [Name]
• Institute for Water Resources Management, [Research Institution]

Short Summary

This study presents a multi-criteria framework to improve flood susceptibility modeling by selecting relevant conditioning factors. The proposed framework enhances predictive performance while reducing input data complexity.

Objective

• Investigate the effect of factor selection on flood susceptibility modeling

Study Configuration

• Spatial Scale: Watershed scale, with focus on rapidly urbanizing areas
• Temporal Scale: Historical (long-term) and current conditions

Methodology and Data

• Models used:
  • Frequency Ratio (FR)
  • Shannon Entropy (SE)
  • Certainty Factor (CF)
  • VIKOR
• Data sources:
  • Flood inventory of 230 historical flood locations
  • Satellite and observation data for land-use and environmental factors

Main Results

• The proposed framework reduced conditioning factors from 19 to 9.
• Optimized models showed improved predictive performance, with the FR model achieving a Success Rate AUC of 90.41% and Prediction Rate AUC of 89.88%.
• The CF model achieved the highest overall performance after optimization.

Contributions

This study contributes to existing literature by providing an effective framework for selecting relevant conditioning factors in flood susceptibility modeling, leading to improved predictive performance and reduced input data complexity.

Funding

• This research was funded by the [Project Name] (Grant Code: [Reference Code])
• Supported by the [Program Name] (Grant Code: [Reference Code])

Citation

@article{Elsadek2026Improving,
  author = {Elsadek, Wael M. and Kanae, Shinjiro},
  title = {Improving Flood Susceptibility Modeling Through a Multi-Criteria Feature Selection Framework},
  journal = {Water},
  year = {2026},
  doi = {10.3390/w18192411},
  url = {https://doi.org/10.3390/w18192411}
}