Frequency ratio model for flood susceptibility mapping in Nigeria
Oluwadamilola Oluwatoyin Hazzan1,2,3, Jinxi Song4,5,6, Collins Chimezie Elendu7
1Xi'an Key Laboratory of Environmental Simulation and Ecological Health in the Yellow River Basin, College of Urban and Environmental Sciences, Northwest University, Xi'an, 710127, China.
Scientific Reports
|May 6, 2026
Summary
The Niger Delta faces severe annual flooding. This study uses Geographic Information Systems (GIS) and the Frequency Ratio (FR) model to map flood susceptibility, identifying 71.79% of the region as moderately to very high risk.
Area of Science:
- Environmental Science
- Geographic Information Systems
- Disaster Management
Background:
- The Niger Delta region in Nigeria experiences recurrent, severe flooding annually, causing significant damage.
- Accurate flood susceptibility assessment and risk mapping are essential for effective mitigation and planning in this vulnerable area.
- A significant research gap exists regarding documented flood-susceptibility assessments in the economically vital Niger Delta.
Purpose of the Study:
- To delineate flood hazard zones and identify high-risk areas within the Niger Delta region.
- To apply an integrated Geographic Information Systems (GIS) and Frequency Ratio (FR) approach for flood susceptibility assessment.
- To establish a robust framework for proactive flood risk management and sustainable development.
Main Methods:
- Utilized an integrated Geographic Information Systems (GIS) and Frequency Ratio (FR) approach.
- Analyzed ten multicollinearity-free flood-conditioning factors: rainfall, distance to river, drainage density, land use/land cover, elevation, slope, NDVI, soil type, curvature, and topographic wetness index.
- Generated a flood susceptibility map classified into five distinct levels, validated using ROC-AUC analysis.
Main Results:
- 71.79% of the Niger Delta region falls within the moderate to very high flood susceptibility category.
- Bayelsa, Rivers, and Delta States were identified as the most at-risk areas.
- The FR model demonstrated a high predictive accuracy with a reliability rate of 83.16%.
Conclusions:
- The study provides critical, data-driven insights for policymakers and urban planners in flood-prone regions.
- Findings support a transition from reactive disaster response to proactive flood risk management strategies.
- The developed framework is transferable and applicable to similar flood-prone areas globally for enhanced mitigation and development.
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