Comparative Analysis of Artificial Neural Networks (ANNs) and Support Vector Machine (SVM) Models for Flood Forecasting in the Vaal River Basin, South Africa

Background/Purpose: Flooding poses severe risks to South Africa's Vaal River Basin, a critical water source for over 12 million people and South Africa's economic heartland. Accurate flood prediction is essential for early warning, water resource management, and disaster preparedness.,Methods: This study presents the first comprehensive comparative evaluation of Artificial Neural Network (ANN) and Support Vector Machine (SVM) models against physically based hydrological models, the Hydrologic Engineering Center's Hydrologic Modelling System (HEC-HMS) and the Soil and Water Assessment Tool Plus (SWAT+), for flood prediction in the Vaal River Basin. A 30-year hydrometeorological dataset (1994–2024) from the South African Weather Service and the Department of Water and Sanitation was used. Model performance was evaluated using Nash–Sutcliffe Efficiency (NSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and additional flood-specific metrics.,Key Findings: During calibration (1994–2009), the ANN model achieved the highest performance (NSE = 0.87, R² = 0.89, RMSE = 125.3 m³/s), followed by SVM (NSE = 0.84), HEC-HMS (NSE = 0.78), and SWAT+ (NSE = 0.74). During validation (2010–2024), ANN maintained the highest NSE (0.82) at 1–3-day prediction horizons, while HEC-HMS outperformed all models at medium-term (4–7 days, NSE = 0.71) and long-term (8–15 days, NSE = 0.63) horizons. Critically, HEC-HMS achieved 100% flood detection for extreme events (Q ≥ Q₂₅), compared to only 50% for both AI models. The optimal ANN configuration employed tansig–tansig activation functions with the Levenberg–Marquardt algorithm (RMSE = 6.245, R = 0.832 during training). The SVM with an RBF kernel achieved moderate training accuracy (RMSE = 11.523, R = 0.705).,Conclusions: No single model consistently outperforms across all conditions. AI models are optimal for short-term forecasting of moderate-to-significant floods, whereas physically based models, particularly HEC-HMS, are indispensable for extreme events and extended forecast horizons. A hybrid ensemble framework is recommended for operational flood management in the Vaal River Basin.