Hydrological time-series modelling using an adaptive neuro-fuzzy inference system

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.