Imam Suprayogi, Joleha Joleha, Nurdin Nurdin


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.

Full Text:



[Chang F.J, Chang Y.C. Counter Propagation Fuzzy-Neural Network for River Flow Reconstruction, Hydrolic Processes , 2001; 15, 219-232.

Chang F.J, Chang Y.T. Adaptive Neuro Fuzzy Inference System for Prediction Water Level in Reservoir, Advances in Water Resources 29, 2006,1-10.

Cheng S.H, Lin Y.H, Chang L.C, Chang F.J., The Strategy of Building a Flood Forecast Model by Neuro Fuzzy Network, Hydrolic Process, 2006; 20, 525-1540.

Ertunga C.O, Duckstein L. Fuzzy Conceptual Rainfall-Runoff Models. Journal of Hydrology. 2001; 253: 41-68.

Jang J.S.R. ANFIS : Adaptive Network Based Fuzzy Inference System. Journal of IEEE Transactions System, Man and Cybernetics. 1993; 23 (3), 655-685.

Jang J.S.R, Sun C.T, Mizutani E. Neuro-Fuzzy and Softcomputing. New York : Springer Englewood Cliffs, 1997: 607-620.

Liong S.Y, Lim W.H, Kojiri T, Hori T. Advance Flood Forecasting for Flood Stricken Bangladesh with Fuzzy Reason Method. Hydrolic Processes , 2000; 14, 431- 448.

Mahabir P.C., Hick F.E., Fayek A.R . Application of Fuzzy Logic to the Seasonal Runoff, Hydrolic Processes , 2000; 17, 3749-3762.

Mitra B, Scott F.E, Fayek A.R . Application of Fuzzy Logic to the Prediction of Soil Erosion in a Large Watershed, Geoderma, 1998; 86, 183-209.

Nayak P.C., Sudheer K.P, Ramasastri., Fuzzy Computing Based Rainfall- Runoff Model for Real Time Flood Forecasting, Hydrolic Process, 2004 a; 17, 3749-3762.

Nayak P.C, Sudheer, K.P, Ramasastri, A Neuro Fuzzy Computing Technique for Modelling Hydrological Time Series, Journal of Hydrology, 2004b; 29, 52-56.

See L, Shaw O. Using Softcomputing Techniques to Enhance Flood Forecasting on The River Ouse. In: Proceding Hydroinformatics (ed. by V. Babovic & L. C. Larsen), Rotterdam. 1998; 98 : 819-824.

See L, Shaw O. Applying Artificial Neural Network Approach to River Level Forecasting. Journal de Sciences Hydrologiques. 1999; 44(5) : 763-778.


  • There are currently no refbacks.


Kampus Raja Ali Haji
Gedung Fakultas Sains & Teknologi UIN Suska Riau
Jl.H.R.Soebrantas No.155 KM 18 Simpang Baru Panam, Pekanbaru 28293