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Artificial neural networks for daily rainfall—runoff modelling |
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| Authors: | M P RAJURKAR U C KOTHYARI U C CHAUBE |
| Institution: | 1. Water Resources Development and Training Centre, Indian Institute of Technology (IIT) Roorkee (formerly University of Roorkee) , Roorkee, 247 667, India;2. Department of Civil Engineering , IIT Roorkee , Roorkee, 247 667, India E-mail: umeshfce@iitr.ernet.in;3. Water Resources Development and Training Centre, IIT Roorkee , Roorkee, 247 667, India |
| Abstract: | Abstract The application of artificial neural network (ANN) methodology for modelling daily flows during monsoon flood events for a large size catchment of the Narmada River in Madhya Pradesh (India) is presented. The spatial variation of rainfall is accounted for by subdividing the catchment and treating the average rainfall of each subcatchment as a parallel and separate lumped input to the model. A linear multiple-input single-output (MISO) model coupled with the ANN is shown to provide a better representation of the rainfall-runoff relationship in such large size catchments compared with linear and nonlinear MISO models. The present model provides a systematic approach for runoff estimation and represents improvement in prediction accuracy over the other models studied herein. |
| Keywords: | artificial neural network multiple-input single-output models nonlinear models rainfall-runoff modelling |