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• W2498073538 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." @default.
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• W2498073538 date "2016-12-01" @default.
• W2498073538 modified "2023-10-04" @default.
• W2498073538 title "Hydrological flow rate estimation using artificial neural networks: Model development and potential applications" @default.
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• W2498073538 doi "https://doi.org/10.1016/j.amc.2016.07.014" @default.
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