Developing an Equation-Based Model for Accurate Flood Risk Prediction

As urbanization intensifies and climate change increases extreme weather events, the demand for accurate, localized flood prediction tools has become increasingly urgent. This study introduces a robust, equation-based flood risk prediction model designed to quantify and forecast flood risk in urban environments. Developed using a data-driven, multi-factor framework, the model integrates geospatial analysis, geotechnical soil data, and multi-criteria decision-making (MCDM) through the Analytic Hierarchy Process (AHP). Applied to a Nigerian case study, the model incorporates eight critical variables - slope, elevation, rainfall, land use/land cover, river proximity, soil type, drainage density, and road proximity. Each factor was assigned an AHP-derived weight, with a consistency ratio of 4%, indicating high methodological reliability. The resulting regression model: Y = −19.96 − 0.01(Elevation) − 8.38(Slope) + 175.64(Drainage Density) + 13.13(Rainfall) − 3.8×10⁻⁵(River Proximity) + 0.07(Soil Type) achieved a strong coefficient of determination (R² = 0.804), explaining 80.4% of flood risk variability. Prediction accuracy was further validated with a Mean Absolute Error of 0.002 and 81.1% of predictions falling within ±0.5 error margins. Laboratory soil testing validated spatial inputs, and alignment with historical flood data confirmed the model’s predictive capability. Beyond its technical performance, the model is scalable, adaptable, and applicable across diverse urban and environmental settings. By accurately capturing the interaction between terrain, climate, and human factors, this model provides a powerful tool for urban planners, engineers, and policymakers aiming to safeguard communities from flood hazards.