Precipitation-Runoff Modeling Using Artificial Neural Networks and Conceptual Models

Abstract

Inspired by the functioning of the brain and biological nervous systems, artificial neural networks (ANNs) have been applied to various hydrologic problems in the last 10 years. In this study, ANN models are compared with traditional conceptual models in predicting watershed runoff as a function of rainfall, snow water equivalent, and temperature. The ANN technique was applied to model watershed runoff in three basins with different climatic and physiographic characteristics—the Fraser River in Colorado, Raccoon Creek in Iowa, and Little Patuxent River in Maryland. In the Fraser River watershed, the ANN technique was applied to model monthly streamflow and was compared to a conceptual water balance (Watbal) model. The ANN technique was used to model the daily rainfall-runoff process and was compared to the Sacramento soil moisture accounting (SAC-SMA) model in the Raccoon River watershed. The daily rainfall-runoff process was also modeled using the ANN technique in the Little Patuxent River basin, and the training and testing results were compared to those of a simple conceptual rainfall-runoff (SCRR) model. In all cases, the ANN models provided higher accuracy, a more systematic approach, and shortened the time spent in training of the models. For the Fraser River, the accuracy of monthly streamflow forecasts by the ANN model was significantly higher compared to the accuracy of the Watbal model. The best-fit ANN model performed as well as the SAC-SMA model in the Raccoon River. The testing and training accuracy of the ANN model in Little Patuxent River was comparatively higher than that of the SCRR model. The initial results indicate that ANNs can be powerful tools in modeling the precipitation-runoff process for various time scales, topography, and climate patterns.