کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
4625547 1631765 2016 13 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Hydrological flow rate estimation using artificial neural networks: Model development and potential applications
ترجمه فارسی عنوان
تخمین سرعت جریان هیدرولوژیکی با استفاده از شبکه های عصبی مصنوعی: توسعه مدل و کاربرد بالقوه
کلمات کلیدی
نرخ جریان، شبکه های عصبی مصنوعی، مونت کارلو شبیه سازی، سناریوی هواشناسی، تولید هیدروژن
موضوعات مرتبط
مهندسی و علوم پایه ریاضیات ریاضیات کاربردی
چکیده انگلیسی


• A new model for prediction of flow rate in Lim river basin is developed.
• It is shown that derived model is insensitive to common measurement errors.
• Derived model could be applied for predicting future flows using climate models.
• Model could be used for forecasting the hydroenergy production.

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".

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Applied Mathematics and Computation - Volume 291, 1 December 2016, Pages 373–385
نویسندگان
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