A neuro-fuzzy computing technique for modeling hydrological time series

Intelligent computing tools such as artificial neural network (ANN) and fuzzy logic approaches are proven to be efficient when applied individually to a variety of problems. Recently there has been a growing interest in combining both these approaches, and as a result, neuro-fuzzy computing techniques have evolved. This approach has been tested and evaluated in the field of signal processing and related areas, but researchers have only begun evaluating the potential of this neuro-fuzzy hybrid approach in hydrologic modeling studies. This paper presents the application of an adaptive neuro fuzzy inference system (ANFIS) to hydrologic time series modeling, and is illustrated by an application to model the river flow of Baitarani River in Orissa state, India. An introduction to the ANFIS modeling approach is also presented. The advantage of the method is that it does not require the model structure to be known a priori, in contrast to most of the time series modeling techniques. The results showed that the ANFIS forecasted flow series preserves the statistical properties of the original flow series. The model showed good performance in terms of various statistical indices. The results are highly promising, and a comparative analysis suggests that the proposed modeling approach outperforms ANNs and other traditional time series models in terms of computational speed, forecast errors, efficiency, peak flow estimation etc. It was observed that the ANFIS model preserves the potential of the ANN approach fully, and eases the model building process. q 2004 Elsevier B.V. All rights reserved.

[1]  W. E. Hart,et al.  A critique of current methods in hydrologic systems investigation , 1964 .

[2]  M. Stone Cross‐Validatory Choice and Assessment of Statistical Predictions , 1976 .

[3]  Saburo Ikeda,et al.  Sequential GMDH Algorithm and Its Application to River Flow Prediction , 1976, IEEE Transactions on Systems, Man, and Cybernetics.

[4]  E. Todini,et al.  A stable estimator for linear models: 2. Real world hydrologic applications , 1976 .

[5]  E. Todini,et al.  A stable estimator for linear models: 1. Theoretical development and Monte Carlo Experiments , 1976 .

[6]  Jose D. Salas,et al.  APPROACHES TO MULTIVARIATE MODELING OF WATER RESOURCES TIME SERIES1 , 1985 .

[7]  睦博 藤田,et al.  An Application of Fuzzy Set Theory to Runoff Prediction , 1985 .

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

[9]  Geoffrey E. Hinton,et al.  Learning representations by back-propagating errors , 1986, Nature.

[10]  M. Sugeno,et al.  Structure identification of fuzzy model , 1988 .

[11]  Kurt Hornik,et al.  Multilayer feedforward networks are universal approximators , 1989, Neural Networks.

[12]  Jyh-Shing Roger Jang,et al.  ANFIS: adaptive-network-based fuzzy inference system , 1993, IEEE Trans. Syst. Man Cybern..

[13]  Michio Sugeno,et al.  A fuzzy-logic-based approach to qualitative modeling , 1993, IEEE Trans. Fuzzy Syst..

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

[15]  Mu-lan Zhu,et al.  Long Lead Time Forecast of Runoff Using Fuzzy Reasoning Method , 1994 .

[16]  Tommi Ojala,et al.  Neuro-fuzzy systems in control , 1995 .

[17]  Chuen-Tsai Sun,et al.  Neuro-fuzzy modeling and control , 1995, Proc. IEEE.

[18]  A. Roli Artificial Neural Networks , 2012, Lecture Notes in Computer Science.

[19]  M. J. Hall,et al.  Artificial neural networks as rainfall-runoff models , 1996 .

[20]  Holger R. Maier,et al.  Determining Inputs for Neural Network Models of Multivariate Time Series , 1997 .

[21]  Taha B. M. J. Ouarda,et al.  Comment on “The use of artificial neural networks for the prediction of water quality parameters” by H. R. Maier and G. C. Dandy , 1997 .

[22]  J. Faraway,et al.  Time series forecasting with neural networks: a comparative study using the air line data , 2008 .

[23]  Ulrich Anders,et al.  Model selection in neural networks , 1999, Neural Networks.

[24]  Linda See,et al.  Applying soft computing approaches to river level forecasting , 1999 .

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

[26]  R Govindaraju,et al.  ARTIFICIAL NEURAL NETWORKS IN HYDROLOGY: II, HYDROLOGIC APPLICATIONS , 2000 .

[27]  N. Null Artificial Neural Networks in Hydrology. I: Preliminary Concepts , 2000 .

[28]  Peter Gemmar,et al.  Machine supported Development of Fuzzy - Flood Forecast Systems , 2000 .

[29]  Asaad Y. Shamseldin,et al.  A non-linear combination of the forecasts of rainfall-runoff models by the first-order Takagi–Sugeno fuzzy system , 2001 .

[30]  Sobri Harun,et al.  Rainfall-Runoff Modeling Using Artificial Neural Network , 2001 .

[31]  A. Bárdossy,et al.  Development of a fuzzy logic-based rainfall-runoff model , 2001 .

[32]  Hisashi Shimodaira,et al.  Time-Series Prediction , 2002 .

[33]  K. P. Sudheer,et al.  A data‐driven algorithm for constructing artificial neural network rainfall‐runoff models , 2002 .

[34]  Ashu Jain,et al.  Comparative Analysis of Event-Based Rainfall-Runoff Modeling Techniques—Deterministic, Statistical, and Artificial Neural Networks , 2003 .