Measuring Discharge Using Back-Propagation Neural Network: A Case Study on Brahmani River Basin

  • Dillip K. Ghose
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 695)


Prediction of discharge (runoff) is vital for flood control during peak periods of flow. The present work is focused on the prediction discharge using back-propagation neural network (BPNN) models. Parameters like stage (water level) have been collected on daily basis from Govindpur basins on River Brahmani to estimate discharge using BPNN model. Different architectures of models are trained and tested to predict the performance of models during June, July, and August of monsoon period for measuring discharges at the proposed station. The individual best performances for different models are found out to measure discharges during peak period of monsoon. Among June, July, and August, the model performance says the highest flow occurs during the month of July for the study period.


Stage Discharge Neural networks Back-propagation 


  1. 1.
    Atiken, A.P.: Assesing systematic errors in rainfall-runoff models. J. Hydrol. 20, 121–136 (1973)CrossRefGoogle Scholar
  2. 2.
    Atiya, A.F., El-Shoura, S.M., Shaheen, S.I., El-Sherif, M.S.: A comparison between neural-network forecasting techniques—case study, river flow forecasting. IEEE Trans. Neural Netw. 10(2), 402–409 (1999)CrossRefGoogle Scholar
  3. 3.
    Carriere, P., Mohaghegh, S., Gaskari, R.: Performance of a virtual runoff hydrograph system. J. Water Serv. (1993)Google Scholar
  4. 4.
    Fatima, K., Shaheen, A.: Estimation of surface runoff for Tarbela reservoir. In: ICAST Proceedings of 2nd International Conference on Advances in Space Technologies, Space in the Service of Mankind, 4747695, pp. 103–106. (2008)Google Scholar
  5. 5.
    French, M., Krajewski, W., Cuykendall, R.R.: Rainfall forecasting in space and time using a neural network. J. Hydrol. 137, 1–31 (1992)CrossRefGoogle Scholar
  6. 6.
    Garrote, L., Bras, R.L.: A distributed model for real—time flood forecasting using digital elevation models. J. Hydrol. 167, 279–306 (1995)CrossRefGoogle Scholar
  7. 7.
    Gautam, M.R., Watanabe, K., Saegusa, H.: Runoff analysis in humid forest catchments with artificial neural network. J. Hydrol. 235, 117–136 (2004)CrossRefGoogle Scholar
  8. 8.
    Hino, M.: Prediction of flood and stream flow by modern control and stochastic theories. In: Proceedings of the 2nd International IAHR, Symposium of Stochastic Hydraulic, 5-1-5-26. (1997)Google Scholar
  9. 9.
    Mason, J.C., Price, R.K.: Tem’me, A.: A neural network model of rainfall-runoff using radial basis functions. J. Hydraul. Res. 34(4), 537–548 (1996)CrossRefGoogle Scholar
  10. 10.
    Minns, A.W., Hall, M.J.: Artificial neural networks as rainfall-runoff models. Hydrol. Sci. J. 41(3), 399–417 (1996)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Department of Civil EngineeringNational Institute of TechnologySilcharIndia

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