Using artificial neural networks (ANNs) for sediment load forecasting of Talkherood river mouth
نویسندگان
چکیده
منابع مشابه
Monthly runoff forecasting by means of artificial neural networks (ANNs)
Over the last decade or so, artificial neural networks (ANNs) have become one of the most promising tools formodelling hydrological processes such as rainfall runoff processes. However, the employment of a single model doesnot seem to be an appropriate approach for modelling such a complex, nonlinear, and discontinuous process thatvaries in space and time. For this reason, this study aims at de...
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The integration of wind farms in power networks has become an important problem. As electricity cannot be preserved because of the highest cost of storage, electricity production must following market demand, necessarily. Short-long term wind forecasting over different time steps is becoming an important process for the management of wind farms. Time series modelling of wind speeds is based on ...
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متن کاملmonthly runoff forecasting by means of artificial neural networks (anns)
over the last decade or so, artificial neural networks (anns) have become one of the most promising tools formodelling hydrological processes such as rainfall runoff processes. however, the employment of a single model doesnot seem to be an appropriate approach for modelling such a complex, nonlinear, and discontinuous process thatvaries in space and time. for this reason, this study aims at de...
متن کاملMonthly runoff forecasting by means of artificial neural networks (ANNs)
Over the last decade or so, artificial neural networks (ANNs) have become one of the most promising tools for modelling hydrological processes such as rainfall runoff processes. However, the employment of a single model does not seem to be an appropriate approach for modelling such a complex, nonlinear, and discontinuous process that varies in space and time. For this reason, this study aims at...
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ژورنال
عنوان ژورنال: Journal of Urban and Environmental Engineering
سال: 2009
ISSN: 1982-3932
DOI: 10.4090/juee.2009.v3n1.001006