Flood risk modeling for Kisumu County, Kenya: integrating multi-source geospatial data and machine learning
TL;DR
An integrated framework combining remote sensing, GIS, and machine learning for flood risk mapping and forecasting using multi‐source geospatial data and meteorological data is developed and validated, providing a replicable, data‐driven methodology for flood risk assessment in data‐scarce regions globally.
Abstract
Flooding poses an escalating threat to Kisumu County, Kenya, driven by climate change and rapid urbanization. This study develops an integrated framework combining remote sensing, GIS, and machine learning for flood risk mapping and forecasting using multi‐source geospatial data (Sentinel‐1 SAR, Landsat 8, SRTM DEM, SMAP soil moisture) and meteorological data (2014‐2025). Four machine learning models random forest (RF), artificial neural network (ANN), deep neural network (DNN), and convolutional neural network (CNN) were developed and validated. The ANN achieved the highest accuracy (R 2 = 0.910, RMSE = 0.038). Excluding RF (R 2 = 0.520) from the ensemble improved performance to R 2 = 0.916 (RMSE = 0.035), a 22% error reduction. Flood risk mapping classified Kisumu County into five categories, revealing that 40.9% (857.8 km 2 ) faces moderate to very high risk, with critical hotspots along the Lake Victoria shoreline and in Kisumu City and Ahero. Slope (20%), NDBI (15%), soil moisture (15%), and stream proximity (15%) were identified as dominant flood drivers. Uncertainty quantification revealed low model variance (R 2 σ = 0.006) and demonstrated that aleatoric uncertainty (62%) dominates epistemic uncertainty (38%). This framework provides a replicable, data‐driven methodology for flood risk assessment in data‐scarce regions globally.