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

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  • Avinash Agarwal
  • R. Singh

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

Suggested Citation

  • Avinash Agarwal & R. Singh, 2004. "Runoff Modelling Through Back Propagation Artificial Neural Network With Variable Rainfall-Runoff Data," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 18(3), pages 285-300, June.
    • Handle: RePEc:spr:waterr:v:18:y:2004:i:3:p:285-300
    DOI: 10.1023/B:WARM.0000043134.76163.b9
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    References listed on IDEAS

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    1. Dooge, James C. I., 1973. "Linear Theory of Hydrologic Systems," Technical Bulletins 160041, United States Department of Agriculture, Economic Research Service.
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    Cited by:

    1. Maryam Ghashghaei & Ali Bagheri & Saeed Morid, 2013. "Rainfall-runoff Modeling in a Watershed Scale Using an Object Oriented Approach Based on the Concepts of System Dynamics," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(15), pages 5119-5141, December.
    2. Maria Diamantopoulou & Vassilis Antonopoulos & Dimitris Papamichail, 2007. "Cascade Correlation Artificial Neural Networks for Estimating Missing Monthly Values of Water Quality Parameters in Rivers," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 21(3), pages 649-662, March.
    3. Bhabagrahi Sahoo, 2013. "Field Application of the Multilinear Muskingum Discharge Routing Method," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(5), pages 1193-1205, March.
    4. Abdüsselam Altunkaynak, 2007. "Forecasting Surface Water Level Fluctuations of Lake Van by Artificial Neural Networks," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 21(2), pages 399-408, February.
    5. A. Sohail & K. Watanabe & S. Takeuchi, 2008. "Runoff Analysis for a Small Watershed of Tono Area Japan by Back Propagation Artificial Neural Network with Seasonal Data," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 22(1), pages 1-22, January.
    6. Qiang Fu & Long-Bin Lu & Jin-Bai Huang, 2014. "Numerical Analysis of Surface Runoff for the Liudaogou Drainage Basin in the North Loess Plateau, China," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(13), pages 4809-4822, October.
    7. Rajib Bhattacharjya & Sandeep Chaurasia, 2013. "Geomorphology Based Semi-Distributed Approach for Modelling Rainfall-Runoff Process," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(2), pages 567-579, January.
    8. Marijana Hadzima-Nyarko & Anamarija Rabi & Marija Šperac, 2014. "Implementation of Artificial Neural Networks in Modeling the Water-Air Temperature Relationship of the River Drava," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(5), pages 1379-1394, March.
    9. Muhammad Sulaiman & Ahmed El-Shafie & Othman Karim & Hassan Basri, 2011. "Improved Water Level Forecasting Performance by Using Optimal Steepness Coefficients in an Artificial Neural Network," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 25(10), pages 2525-2541, August.
    10. Raveendra Rai & B. Mathur, 2008. "Event-based Sediment Yield Modeling using Artificial Neural Network," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 22(4), pages 423-441, April.

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