An evaluation of artificial neural network technique for the determination of infiltration model parameters

Infiltration is a key component in the rainfall runoff models employed for runoff prediction. Conventionally, the hydrologists have relied on classical optimization techniques for obtaining the parameters of various infiltration equations. Recently, artificial neural networks ( ANNs) have been proposed as efficient tools for modelling and forecasting. This paper proposes the use of ANNs for calibrating infiltration equations. The ANN consists of rainfall and runoff as the inputs and the infiltration parameters as the outputs. Classical optimization techniques were also employed to determine flow hydrographs for comparison purposes. The performances of both the approaches were evaluated using a variety of standard statistical measures in terms of their ability to predict runoff. The results obtained in this study indicate that the ANN technique can be successfully employed for the purpose of calibration of infiltration equations. The regenerated and predicted storms indicate that the ANN models performed better than the classical techniques. It has been found that the ANNs are capable of performing very well in situations of limited data availability since the differences in the performances of the ANNs trained on partial information and the ANNs trained on the complete information was only marginal and the ANN trained on partial information consisted of a more compact architecture. A wide variety of standard statistical performance evaluation measures are needed to properly evaluate the performances of various ANN models rather than relying on a few global error statistics ( such as RMSE and correlation coefficient) normally employed. (C) 2005 Published by Elsevier B. V.