Monthly total sediment forecasting using adaptive neuro fuzzy inference system |
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Authors: | Mahmut Firat Mahmud Güngör |
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Institution: | (1) Civil Engineering Department, Faculty of Engineering, Pamukkale University, 20017 Denizli, Turkey |
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Abstract: | Accurate forecasting of sediment is an important issue for reservoir design and water pollution control in rivers and reservoirs.
In this study, an adaptive neuro-fuzzy inference system (ANFIS) approach is used to construct monthly sediment forecasting
system. To illustrate the applicability of ANFIS method the Great Menderes basin is chosen as the study area. The models with
various input structures are constructed for the purpose of identification of the best structure. The performance of the ANFIS
models in training and testing sets are compared with the observed data. To get more accurate evaluation of the results ANFIS
models, the best fit model structures are also tested by artificial neural networks (ANN) and multiple linear regression (MLR)
methods. The results of three methods are compared, and it is observed that the ANFIS is preferable and can be applied successfully
because it provides high accuracy and reliability for forecasting of monthly total sediment. |
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