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

Abstract This study examines the comparative performance of Artificial Neural Networks and Elastic Net Regression in predicting surface runoff in urban stormwater catchments. Utilizing the Storm Water Management Model, a dataset of over 100,000 samples was prepared, incorporating diverse catchment parameters such as impervious surface percentage, overland flow path width, slope, Manning’s coefficients, and depression storage depths. Both models were developed and tuned to enhance runoff prediction accuracy. An assessment was carried out to gauge the predictive accuracy, computational efficiency, and ability to withstand data noise of the models. The Artificial Neural Networks demonstrated significant advantages in handling complex, non-linear relationships inherent in hydrological data, resulting in higher predictive accuracy and stability. Elastic Net Regression, while useful, exhibited limitations, particularly in lower runoff scenarios due to its regularization constraints. The outcomes highlight the relative advantages and drawbacks of each modeling technique, offering crucial perspectives for enhancing water resource management in urban settings.