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https://hdl.handle.net/11499/7116
Title: | Hydrological time-series modelling using an adaptive neuro-fuzzy inference system | Authors: | Fırat, Mahmut Güngör, Mahmud |
Keywords: | ANFIS ANN Fuzzy logic Hydrological time-series River great menderes Artificial intelligence Forecasting Fuzzy inference Fuzzy systems Harmonic analysis Modal analysis Neural networks Reusability Signal filtering and prediction Statistics Water resources Adaptive Neuro-Fuzzy Inference System (ANFIS) Applied (CO) Artificial neural network (ANNs) case studies Cross-validation methods Data sets high accuracy In order Input/output (I/O) data Sustainable use Time Series Time series forecasting Training and testing Time series analysis artificial neural network basin analysis forecasting method fuzzy mathematics hydrological modeling input-output analysis numerical model performance assessment time series analysis Eurasia Menderes Basin Turkey |
Abstract: | Accurate forecasting of hydrological time-series is a quite important issue for a wise and sustainable use of water resources. In this study, an adaptive neuro-fuzzy inference system (ANFIS) approach is used to construct a time-series forecasting system. In particular, the applicability of an ANFIS to the forecasting of the time-series is investigated. To illustrate the applicability and capability of an ANFIS, the River Great Menderes, located in western Turkey, is chosen as a case study area. The advantage of this method is that it uses the input-output data sets. A total of 5844 daily data sets collected from 1985 to 2000 are used for the time-series forecasting. Models having various input structures were constructed and the best structure was investigated. In addition, four various training/testing data sets were built by cross-validation methods and the best data set was obtained. The performance of the ANFIS models in training and testing sets was compared with observations and also evaluated. In order to get an accurate and reliable comparison, the best-fit model structure was also trained and tested by artificial neural networks and traditional time-series analysis techniques and the results compared. The results indicate that the ANFIS can be applied successfully and provide high accuracy and reliability for time-series modelling. Copyright © 2007 John Wiley & Sons, Ltd. | URI: | https://hdl.handle.net/11499/7116 https://doi.org/10.1002/hyp.6812 |
ISSN: | 0885-6087 |
Appears in Collections: | Mühendislik Fakültesi Koleksiyonu Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection |
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