ANN and Fuzzy Logic Models for Simulating Event-Based Rainfall-Runoff
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VIEW THE REPLYPublication: Journal of Hydraulic Engineering
Volume 132, Issue 12
Abstract
This study presents the development of artificial neural network (ANN) and fuzzy logic (FL) models for predicting event-based rainfall runoff and tests these models against the kinematic wave approximation (KWA). A three-layer feed-forward ANN was developed using the sigmoid function and the backpropagation algorithm. The FL model was developed employing the triangular fuzzy membership functions for the input and output variables. The fuzzy rules were inferred from the measured data. The measured event based rainfall-runoff peak discharge data from laboratory flume and experimental plots were satisfactorily predicted by the ANN, FL, and KWA models. Similarly, all the three models satisfactorily simulated event-based rainfall-runoff hydrographs from experimental plots with comparable error measures. ANN and FL models also satisfactorily simulated a measured hydrograph from a small watershed in area. The results provide insights into the adequacy of ANN and FL methods as well as their competitiveness against the KWA for simulating event-based rainfall-runoff processes.
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© 2006 ASCE.
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Received: Oct 19, 2004
Accepted: Feb 7, 2006
Published online: Dec 1, 2006
Published in print: Dec 2006
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