Predictive Modeling of Flood-Prone Areas Using SAR and Environmental Variables

TL;DR

This study integrates SAR imagery and environmental data with machine learning to accurately predict flood-prone areas, providing valuable tools for disaster management in data-scarce regions.

Contribution

It introduces a novel approach combining Sentinel-1 SAR data with environmental factors and ensemble ML models for flood susceptibility mapping.

Findings

Random Forest achieved highest accuracy (0.762) among models.

RF model outperformed LR, CART, and SVM in flood prediction.

Flood vulnerability map highlights Kano Plains as high-risk area.

Abstract

Flooding is one of the most destructive natural hazards worldwide, posing serious risks to ecosystems, infrastructure, and human livelihoods. This study combines Synthetic Aperture Radar (SAR) imagery with environmental and hydrological data to model flood susceptibility in the River Nyando watershed, western Kenya. Sentinel-1 dual-polarization SAR data from the May 2024 flood event were processed to produce a binary flood inventory, which served as training data for machine learning (ML) models. Six conditioning factors -- slope, elevation, aspect, land use/land cover, soil type, and distance from streams -- were integrated with the SAR-derived flood inventory to train four supervised classifiers: Logistic Regression (LR), Classification and Regression Trees (CART), Support Vector Machines (SVM), and Random Forest (RF). Model performance was assessed using accuracy, Cohen's Kappa, and Receiver Operating Characteristic (ROC) analysis. Results indicate that RF achieved the highest predictive performance (accuracy = 0.762; Kappa = 0.480), outperforming LR, CART, and SVM. The RF-based susceptibility map showed that low-lying Kano Plains near Lake Victoria have the highest flood vulnerability, consistent with historical flood records and the impacts of the May 2024 event. These findings demonstrate the value of combining SAR data and ensemble ML methods for flood susceptibility mapping in regions with limited data. The resulting maps offer important insights for disaster risk reduction, land-use planning, and early warning system development.