Comparative Analysis of Event-based Rainfall-runoff Modeling Techniques—Deterministic, Statistical, and Artificial Neural Networks

Abstract

Modeling of an event-based rainfall-runoff process has been of importance in hydrology. Historically, researchers have relied on conventional modeling techniques, either deterministic, which consider the physics of the underlying process, or systems theoretic/black box, which do not. This technical note investigates the suitability of some deterministic and statistical techniques along with the artificial neural networks (ANNs) technique to model an event-based rainfall-runoff process. Specifically, two unit hydrograph models, four regression models, and two ANN models were developed. Data derived from Salado Creek at Bitters Road, San Antonio were employed. It was found that the ANN models consistently outperformed conventional models, barring a few exceptions, and provided a better representation of an event-based rainfall-runoff process in general, and better prediction of peak discharge and time to peak discharge, in particular.