Neural Smithing: Supervised Learning in Feedforward Artificial Neural Networks
Neural Smithing: Supervised Learning in Feedforward Artificial Neural Networks
Hybrid neural network models for hydrologic time series forecasting
Applied Soft Computing
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The results of a study using linear artificial neural networks (ANNs) to determine the physical parameters in event-based rainfall-runoff modeling are presented in this paper. The input structure of the ANN was determined based on an analysis of the discretized form of the kinematic wave equations, and the physical parameters were obtained through a back calculation of the weights and biases of the ANN. Two cases were considered; using ANNs trained on datasets derived from: (1) effective rain of entire events and (2) total rain of wet portion of events only. For Case (1), the parameters @Dt and @a(=S/n) were determined with excellent accuracy. For Case (2), in addition to @Dt and @a, the constant loss rate, @F, was also determined with excellent accuracy. Further, the use of total rain of entire events (a common practice in ANN applications in rainfall-runoff modeling) was also investigated, and it was found that the results from this analysis are less realistic compared to those of Case (2).