Runoff Modelling Through Back Propagation Artificial Neural Network With Variable Rainfall-Runoff Data

Avinash Agarwal () and R. Singh

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2004, vol. 18, issue 3, 285-300

Abstract: Multi layer back propagation artificial neural network (BPANN) models have been developed to simulate rainfall-runoff process for two sub-basins of Narmada river (India) viz. Banjar up to Hridaynagar and Narmada up to Manot considering three time scales viz. weekly, ten-daily and monthly with variable and uncertain data sets. The BPANN runoff models were developed using gradient descent optimization technique and were generalized through cross-validation. In almost all cases, the BPANN developed with the data having relatively high variability and uncertainty learned in less number of iterations, with high generalization. Performance of BPANN models is compared with the developed linear transfer function (LTF) model and was found superior. Copyright Kluwer Academic Publishers 2004

Keywords: back propagation artificial neural network; rainfallrunoff modelling; transfer function model (search for similar items in EconPapers)
Date: 2004
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DOI: 10.1023/B:WARM.0000043134.76163.b9

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