Using adaptive neuro-fuzzy inference system for hydrological time series prediction

Conventionally, the multiple linear regression procedure has been known as the most popular models in simulating hydrological time series. However, when the nonlinear phenomenon is significant, the multiple linear will fail to develop an appropriate predictive model. Recently, intelligence system approaches such as artificial neural network (ANN) and neuro-fuzzy methods have been used successfully for time series modelling. In most instances for neural networks, multi layer perceptrons (MLPs) that are trained with the back-propagation algorithm have been used. The major shortcoming of this approach is that the knowledge contained in the trained networks is difficult to interpret. Using neuro-fuzzy approaches, which enable the information that is stored in trained networks to be expressed in the form of a fuzzy rule base, would help to overcome this issue. In the present study, a time series neuro-fuzzy model is proposed that is capable of exploiting the strengths of traditional time series approaches. The aim of this article is to investigate the potential of a neuro-fuzzy system with a Sugeno inference engine, considering different numbers of membership functions. Three rivers have been selected and daily prediction for them was applied. For better judgment, outcomes of the network have been compared to an autoregressive model.

[1]  Souad Riad,et al.  Rainfall-runoff model usingan artificial neural network approach , 2004, Math. Comput. Model..

[2]  Pankaj Singh,et al.  Suitability of different neural networks in daily flow forecasting , 2007, Appl. Soft Comput..

[3]  Hojjat Adeli,et al.  Neural Networks in Civil Engineering: 1989–2000 , 2001 .

[4]  E. Toth,et al.  Comparison of short-term rainfall prediction models for real-time flood forecasting , 2000 .

[5]  Hikmet Kerem Cigizoglu,et al.  Suspended sediment load simulation by two artificial neural network methods using hydrometeorological data , 2007, Environ. Model. Softw..

[6]  K.-Peter Holz,et al.  Short-term water level prediction using neural networks and neuro-fuzzy approach , 2003, Neurocomputing.

[7]  U. C. Kothyari,et al.  Modeling of the daily rainfall-runoff relationship with artificial neural network , 2004 .

[8]  W. G. V. BALCHIN.  Water Resources of the United States , 1961, Nature.

[9]  Michio Sugeno,et al.  Fuzzy identification of systems and its applications to modeling and control , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

[10]  P. L. Stark.  The United States geological survey website , 1997 .

[11]  Holger R. Maier,et al.  Forecasting cyanobacterium Anabaena spp. in the River Murray, South Australia, using B-spline neurofuzzy models , 2001 .

[12]  Ebrahim H. Mamdani,et al.  An Experiment in Linguistic Synthesis with a Fuzzy Logic Controller , 1999, Int. J. Hum. Comput. Stud..

[13]  Özlem Terzi,et al.  Estimating Evaporation Using ANFIS , 2006 .

[14]  Vijay P. Singh,et al.  Predicting Longitudinal Dispersion Coefficient in Natural Streams by Artificial Neural Network , 2005 .

[15]  Soichi Nishiyama,et al.  Analysis and prediction of flow from local source in a river basin using a Neuro-fuzzy modeling tool. , 2007, Journal of environmental management.

[16]  Dong-Sheng Jeng,et al.  Bayesian neural networks for prediction of equilibrium and time-dependent scour depth around bridge piers , 2007, Adv. Eng. Softw..

[17]  Olaf Wolkenhauer.  Data engineering - fuzzy mathematics in systems theory and data analysis , 2001 .

[18]  P. B. Deolalikar,et al.  Neural Networks for Estimation of Scour Downstream of a Ski-Jump Bucket , 2005 .

[19]  Ian Flood,et al.  Neural Networks in Civil Engineering. I: Principles and Understanding , 1994 .

[20]  Vijay K. Rohatgi,et al.  Advances in Fuzzy Set Theory and Applications , 1980 .

[21]  Roberto Baratti,et al.  River flow forecast for reservoir management through neural networks , 2003, Neurocomputing.

[22]  S. M. Bateni,et al.  Neural network and neuro-fuzzy assessments for scour depth around bridge piers , 2007, Eng. Appl. Artif. Intell..

[23]  Paresh Deka,et al.  Fuzzy Neural Network Model for Hydrologic Flow Routing , 2005 .

[24]  Hikmet Kerem Cigizoglu,et al.  Generalized regression neural network in modelling river sediment yield , 2006, Adv. Eng. Softw..

[25]  Yen-Chang Chen,et al.  A counterpropagation fuzzy-neural network modeling approach to real time streamflow prediction , 2001 .

[26]  L. Bodri,et al.  Prediction of extreme precipitation using a neural network: application to summer flood occurence in Moravia , 2000 .

[27]  Yahachiro Tsukamoto,et al.  AN APPROACH TO FUZZY REASONING METHOD , 1993 .