Comparison of ANNs and Empirical Approaches for Predicting Watershed Runoff

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Abstract
Prediction of watershed runoff resulting from precipitation events is of great interest to hydrologists. The nonlinear response of a watershed (in terms of runoff) to rainfall events makes the problem very complicated. In addition, spatial heterogeneity of various physical and geomorphological properties of a watershed cannot be easily represented in physical models. In this study, artificial neural networks (ANNs) were utilized for predicting runoff over three medium-sized watersheds in Kansas. The performances of ANNs possessing different architectures and recurrent neural networks were evaluated by comparisons with other empirical approaches. Monthly precipitation and temperature formed the inputs, and monthly average runoff was chosen as the output. The issues of overtraining and influence of derived inputs were addressed. It appears that a direct use of feedforward neural networks without time-delayed input may not provide a significant improvement over other regression techniques. However, inclusion of feedback with recurrent neural networks generally resulted in better performance.