A Machine Learning Framework for Flood Risk Classification and Early warning in River Catchment Areas

Abstract

Flooding remains one of the most destructive natural hazards in Uganda, with the River Man afwa catchment area on the slopes of Mount Elgon experiencing recurrent and severe flood events that displace communities, destroy infrastructure, and disrupt livelihoods. Existing flood forecasting in Uganda relies heavily on global systems such as GloFAS and ECMWF, which operate at low spatial resolution and are often unreliable for small catchments, limiting their effectiveness for local early warning and disaster preparedness. This study developed a machine learning framework for flood risk classification in the River Manafwa catchment, using historical rainfall data (CHIRPS satellite, 1989–2024) and observed river discharge data (Min istry of Water and Environment, 1989–2024). Hydrological features were engineered, including antecedent rainfall accumulations (1, 3, 5, and 7-day windows), lagged and rolling-mean dis charge variables, discharge differencing and acceleration terms, and seasonal cyclical indicators. A locally calibrated four-level flood risk classification (Normal, Mild, Advanced, Extreme) was developed based on discharge percentile thresholds (Q75, Q90, and Q95). Five machine learn ing models, Logistic Regression, Decision Tree, Random Forest, Gradient Boosting, and MLP Neural Network, were trained and evaluated using Macro F1-score, Extreme F1-scores, and AUC-ROC, with SMOTE applied to address class imbalance in the training data. Gradient Boosting achieved the strongest overall performance, recording the highest Macro F1-score and Extreme-class F1-score following SMOTE application, and was selected as the most suitable model for this study. SHAP analysis identified short-term lagged discharge, particularly the previous day’s discharge, as the dominant indicator of flood risk across all classes, while same day rainfall and short-term cumulative rainfall accumulations retained comparatively greater influence specifically within the Extreme risk class. The model was validated against three documented historical flood events (June 2019, October 2019, and September 2021), with the model successfully flagging Extreme risk conditions several days ahead of officially reported flood dates. The resulting framework offers a low-cost, replicable, data-driven approach to strengthen flood early warning systems for the Manafwa catchment and other flood-prone rivers in Uganda, supporting disaster preparedness agencies including URCS, UNMA, and the OPM’s Department of Relief and Disaster Preparedness.