Comparison of the Effectiveness of Artificial Neural Networks and Elastic Net Regression in Surface Runoff Modeling

This study compares Artificial Neural Networks (ANN) and Elastic Net regression for predicting surface runoff in urban stormwater catchments. Both models were trained on a data set derived from the Stormwater Management Model that included parameters such as imperviousness, flow path width, slope, Manning coefficients, and depression storage. ANN exhibited greater predictive accuracy and stability, especially when modeling nonlinear hydrologic interactions, while Elastic Net offered faster inference and clearer interpretability, but showed reduced accuracy in low-flow conditions. Validation on real-world data revealed the sensitivity of the models to scenarios not fully represented during training. Despite higher computational demands, the ANN proved more adaptable, while the more resource-efficient Elastic Net remains suitable for time-critical or large-scale applications. These findings provide practical insights for urban water resource management, indicating when each approach can be most effectively used in flood risk assessment and stormwater infrastructure planning.