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Hydrological flow rate estimation using artificial neural networks: Model development and potential applications

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  • Kostić, Srđan
  • Stojković, Milan
  • Prohaska, Stevan

Abstract

In present paper we develop a model of monthly river flow rate using artificial neural networks, based on the assumption that air temperature and precipitation predetermine the flow rate dynamics. In order to create a reliable prediction model, we used monthly observations made at Lim river basin in southwestern Serbia from 1950 to 2012, since results of this analysis could be significant for hydro-energy production at ``Potpeć" and ``Brodarevo" hydropower plants. Analysis was conducted using multilayer feed-forward perceptron with Levenberg–Marquardt learning algorithm and appropriate number of hidden neurons which provide the most reliable prediction accuracy. Analysis of derived models with different number of hidden nodes indicate that models are insensitive to the number of hidden units. Model with eight hidden nodes was chosen as the most reliable one, providing highest prediction accuracy (the highest values of determination coefficient and Nash–Sutcliffe coefficient). Predictive power of the developed model was tested against the recordings made in period 1991–2012, providing satisfying prediction accuracy. Moreover, Monte-Carlo simulation showed that prediction accuracy of developed models is robust against expected experimental error, confirming that derived models provide reliable predictions of flow rates, which could be used for water management plans and strategies. We also propose two potential applications of derived model: for predicting the future flow rate using the predefined climate models, and for forecasting the hydroenergy production, on the basis of the linear dependence of the observed flow rate and previously produced electric power. These application are verified for the regional climate model EBU-POM, for the period 2013–2100, and using the data on electric power production at hydro powerplant ``Potpeć".

Suggested Citation

  • Kostić, Srđan & Stojković, Milan & Prohaska, Stevan, 2016. "Hydrological flow rate estimation using artificial neural networks: Model development and potential applications," Applied Mathematics and Computation, Elsevier, vol. 291(C), pages 373-385.
    • Handle: RePEc:eee:apmaco:v:291:y:2016:i:c:p:373-385
    DOI: 10.1016/j.amc.2016.07.014
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    References listed on IDEAS

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    1. D. Nagesh Kumar & K. Srinivasa Raju & T. Sathish, 2004. "River Flow Forecasting using Recurrent Neural Networks," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 18(2), pages 143-161, April.
    2. Muhammad Aqil & Ichiro Kita & Akira Yano & Soichi Nishiyama, 2007. "Neural Networks for Real Time Catchment Flow Modeling and Prediction," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 21(10), pages 1781-1796, October.
    3. Fister, Iztok & Ljubič, Karin & Suganthan, Ponnuthurai Nagaratnam & Perc, Matjaž & Fister, Iztok, 2015. "Computational intelligence in sports: Challenges and opportunities within a new research domain," Applied Mathematics and Computation, Elsevier, vol. 262(C), pages 178-186.
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    1. Jafarian, Ahmad & Measoomy Nia, Safa & Khalili Golmankhaneh, Alireza & Baleanu, Dumitru, 2018. "On artificial neural networks approach with new cost functions," Applied Mathematics and Computation, Elsevier, vol. 339(C), pages 546-555.

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