TITLE:
Multi-Temporal Sentinel-1 Synthetic Aperture Radar and Machine Learning for Flood Susceptibility Assessment of Urban Roads in Nairobi City County, Kenya
AUTHORS:
Patrick Kathiari Gitile, Josphat K. Z. Mwatelah
KEYWORDS:
Urban Flooding, Flood Susceptibility, Sentinel-1 SAR, Machine Learning, Road Network, Nairobi City County
JOURNAL NAME:
Journal of Geoscience and Environment Protection,
Vol.14 No.11,
November
10,
2026
ABSTRACT: Urban flooding increasingly disrupts transport systems in rapidly urbanizing cities, yet road-oriented flood-susceptibility assessments that integrate recurrent flood observations with geographically separated model evaluation remain limited in Nairobi City County, Kenya. This study developed a road-oriented flood-susceptibility framework integrating multi-temporal Sentinel-1 Synthetic Aperture Radar (SAR), Geographic Information Systems (GIS), and supervised machine learning. Fourteen selected flood events from 2017 to 2023 were mapped using pre-event and during-event VV backscatter changes, event-specific statistical thresholds, and permanent-water masking. The resulting event maps were combined to generate a flood-frequency surface ranging from 0 to 14 events, which was converted into a binary modelling response. A road-corridor sampling framework generated 4,372,735 observations, of which 3,076,758 from 10 constituencies were used for training, and 1,295,977 from seven geographically separated constituencies were reserved for testing. Sixteen predictors representing terrain, hydrology, rainfall, land cover, urban development, road characteristics, and background Sentinel-1 radar behaviour were evaluated using Logistic Regression, Random Forest, Gradient Boosting, HistGradientBoosting, and XGBoost. XGBoost achieved a test accuracy of 0.7505, F1-score of 0.7269, balanced accuracy of 0.7493, and ROC-AUC of 0.8257. SHAP analysis identified SAR Mean, SAR Standard Deviation, digital elevation, building density, and rainfall among the most influential predictors. The resulting model classified 7235.952 km of road network into five relative susceptibility classes. Temporal assessment using separately mapped 2024 flood observations identified 944.609 km of flooded road length, of which 423.584 km (44.84%) occurred in the High and Very High classes and 634.780 km (67.20%) occurred in the Moderate-to-Very-High classes. These findings demonstrate the potential of integrating recurrent SAR observations and machine learning for screening urban roads for further flood investigation. The resulting susceptibility map represents relative spatial differences within the assessed road network rather than calibrated flood probabilities or predictions of flood depth, velocity, or duration.