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Title of Journal: Water Resour Manage

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Abbravation: Water Resources Management

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Springer Netherlands

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DOI

10.1007/s11094-015-1232-x

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ISSN

1573-1650

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A neurofuzzy model for inflow forecasting of the

Authors: Ahmed ElShafie Mahmoud Reda Taha Aboelmagd Noureldin
Publish Date: 2006/06/29
Volume: 21, Issue: 3, Pages: 533-556
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Abstract

River flow forecasting is an essential procedure that is necessary for proper reservoir operation Accurate forecasting results in good control of water availability refined operation of reservoirs and improved hydropower generation Therefore it becomes crucial to develop forecasting models for river inflow Several approaches have been proposed over the past few years based on stochastic modeling or artificial intelligence AI techniquesIn this article an adaptive neurofuzzy inference system ANFIS model is proposed to forecast the inflow for the Nile River at Aswan High Dam AHD on monthly basis A major advantage of the fuzzy system is its ability to deal with imprecision and vagueness in inflow database The ANFIS model divides the input space into fuzzy subspaces and maps the output using a set of linear functions A historical database of monthly inflows at AHD recorded over the past 130 years is used to train the ANFIS model and test its performance The performance of the ANFIS model is compared to a recently developed artificial neural networks ANN model The results show that the ANFIS model was capable of providing higher inflow forecasting accuracy specially at extreme inflow events compared with that of the ANN model It is concluded that the ANFIS model can be quite beneficial in water management of Lake Nasser reservoir at AHD

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