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A Virtual Runoff Hydrograph System (VROHS) based on artificial neural network technology was designed and developed to generate runoff hydrograph. Data from 45 lab experiment sets were used to develop the VROHS. A recurrent back-propagation neural network was trained to generate runoff hydrograph. Twenty-nine of the 45 lab experiment sets were randomly selected to train the network, while 16 experiment sets were selected to test the VROHS. It was found that the VROHS could predict the runoff hydrograph system very accurately for sets of input data (experimental conditions) that it had never seen before. The values of the correlation coefficients and coefficient of determination for the testing sets ranged from 0.96 to 0.99 and 0.92 to 0.99, respectively. These high coefficient values demonstrated the good correlation between the observed data and the predicted data and also the high performance of the VROHS.
Carriere et al. (Fri,) studied this question.