<![CDATA[River level forecasting is quite important for reservoir operation studies, flood planning and control, modeling and management water resources. In the last decade, the softcomputing model as a branch of the artificial intelligence science were introduction as a forecast tool beside knowledge based system, expert system, fuzzy logic, artificial neural network, and genetic algorithm. The method that used in this research was a combination between fuzzy logic and artificial neural network which usually called neuro fuzzy system of adaptive neuro fuzzy inference system (ANFIS) algorithm approach was used construct a river level forecasting system. The advantages of this method is that is use input- output data sets. In particular, the applicability of ANFIS as an estimation model for river flow was investigated. To illustrate the applicability and capability the ANFIS, the River Indragiri, located the Indragiri Hulu Residence and the most important water resources of Indragiri catchment’s, was choosen as a case study area. To totally 1997-2008 annual data sets collected years were used to estimate the River level. The models having various input structures were constructed and the best structure was investigated. In addition four various training / testing data were constructed by cross validation methods and the best data set was investigated. The performance of the ANFIS models in training and testing sets were compared with the observation and also evaluated. The results indicated that the ANFIS can be applied successfully and provide high accuracy and reliability for River level estimation in Indragiri River. Keywords : Forecasting, River level, Artificial Neural Network, Fuzzy Logic, Adaptive Neuro Fuzzy Inference System.]]>
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