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Please use this identifier to cite or link to this item: http://repository.iitr.ac.in/handle/123456789/9252
Title: Streamflow forecasting using Artificial Neural Network
Authors: Roba F.G.
Arya, Dhyan Singh
Goel, Narendra Kumar
Published in: Water and Energy International
Abstract: Physical processes, which influence the occurrence of flow in streams, are highly complex and uncertain. Therefore it becomes difficult to fit them in deterministic models. Such problems, however, can be tackled efficiently by the Artificial Neural Network (ANN) approach because of its in-built mechanism of growing wiser with experience. ANNs are capable of adapting the complexity of input-output patterns and accuracy goes on increasing as more and more data are available to them. This paper presents the development and application of an artificial neural network based streamflow forecasting model for Negara river basin, Indonesia. A three-layer network is developed having one input, one hidden and one output layer. Training is conducted using Feed-forward Back Propagation algorithm where the inputs and target outputs are presented to the network as a series of learning sets. The rainfall and flow data from 1978 to 1983 are used for training the network and 1984 data are used for testing the performance of ANN in streamflow forecasting with one day lead time. The efficiency of ANN model is more than 90% for nine months (i.e. December to August) months in which most of the flow occurs. For the remaining three months also the efficiency of forecast is more than 68%. This clearly establishes the fact that ANN can be efficiently used as an alternative to the conventional stream flow forecasting methods.
Citation: Water and Energy International (2000), 57(1): 30-37
URI: http://repository.iitr.ac.in/handle/123456789/9252
Issue Date: 2000
Keywords: Artificial neural network
Feed forward back propagation algorithm
Streamflow forecasting
ISSN: 0972057X
Author Scopus IDs: 6506305391
7004803272
36190650800
Author Affiliations: Roba, F.G., Department of Hydrology, University of Roorkee, Roorkee, India
Arya, D.S., Department of Hydrology, University of Roorkee, Roorkee, India
Goel, N.K., Department of Hydrology, University of Roorkee, Roorkee, India
Corresponding Author: Roba, F.G.; Department of Hydrology, University of Roorkee, Roorkee, India
Appears in Collections:Journal Publications [HY]

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