Adaptive neuro-fuzzy inference system for prediction of water level in reservoir

Accurate prediction of the water level in a reservoir is crucial to optimizing the management of water resources. A neuro-fuzzy hybrid approach was used to construct a water level forecasting system during flood periods. In particular, we used the adaptive network-based fuzzy inference system (ANFIS) to build a prediction model for reservoir management. To illustrate the applicability and capability of the ANFIS, the Shihmen reservoir, Taiwan, was used as a case study. A large number (132) of typhoon and heavy rainfall events with 8640 hourly data sets collected in past 31 years were used. To investigate whether this neuro-fuzzy model can be cleverer (accurate) if human knowledge, i.e. current reservoir operation outflow, is provided, we developed two ANFIS models: one with human decision as input, another without. The results demonstrate that the ANFIS can be applied successfully and provide high accuracy and reliability for reservoir water level forecasting in the next three hours. Furthermore, the model with human decision as input variable has consistently superior performance with regard to all used indexes than the model without this input.

[1]  Christian W. Dawson,et al.  An artificial neural network approach to rainfall-runoff modelling , 1998 .

[2]  W. L. Lane,et al.  Applied Modeling of Hydrologic Time Series , 1997 .

[3]  Stephen L. Chiu,et al.  Fuzzy Model Identification Based on Cluster Estimation , 1994, J. Intell. Fuzzy Syst..

[4]  Thomas E. Croley,et al.  Using Meteorology Probability Forecasts in Operational Hydrology , 2000 .

[5]  Witold F. Krajewski,et al.  Rainfall forecasting in space and time using a neural network , 1992 .

[6]  J. ...,et al.  Applied modeling of hydrologic time series , 1980 .

[7]  Kuolin Hsu,et al.  Artificial Neural Network Modeling of the Rainfall‐Runoff Process , 1995 .

[8]  Y. Nagayama,et al.  Reservoir operation using the neural network and fuzzy systems for dam control and operation support , 2002 .

[9]  G. Visconti,et al.  A Neural Network Approach to Real-Time Rainfall Estimation for Africa Using Satellite Data , 2003 .

[10]  Milan S. Ćalović,et al.  Neuro-fuzzy controller of low head hydropower plants using adaptive-network based fuzzy inference system , 1997 .

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

[12]  null null,et al.  Artificial Neural Networks in Hydrology. II: Hydrologic Applications , 2000 .

[13]  Fakhri Karray,et al.  Minimizing variance of reservoir systems operations benefits using soft computing tools , 2003, Fuzzy Sets Syst..

[14]  M.E. El-Hawary,et al.  Power system dynamic load modeling using adaptive-network-based fuzzy inference system , 1999, Engineering Solutions for the Next Millennium. 1999 IEEE Canadian Conference on Electrical and Computer Engineering (Cat. No.99TH8411).

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

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

[17]  Athanasios Sfetsos,et al.  A comparison of various forecasting techniques applied to mean hourly wind speed time series , 2000 .

[18]  J. Y. Li,et al.  Short‐term inflow forecasting using an artificial neural network model , 2002 .

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

[20]  Samuel O. Russell,et al.  Reservoir Operating Rules with Fuzzy Programming , 1996 .

[21]  Ivica Kostanic,et al.  Principles of Neurocomputing for Science and Engineering , 2000 .

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

[23]  H. Joel Trussell,et al.  Fuzzy inference systems implemented on neural architectures for motor fault detection and diagnosis , 1999, IEEE Trans. Ind. Electron..

[24]  Li-Chiu Chang,et al.  Real‐time recurrent learning neural network for stream‐flow forecasting , 2002 .

[25]  Li-Chiu Chang,et al.  Intelligent control for modeling of real‐time reservoir operation, part II: artificial neural network with operating rule curves , 2005 .

[26]  S. Lallahem,et al.  Evaluation and forecasting of daily groundwater outflow in a small chalky watershed , 2003 .

[27]  Yen-Ming Chiang,et al.  A two‐step‐ahead recurrent neural network for stream‐flow forecasting , 2004 .

[28]  V. Chandramouli,et al.  Deriving a General Operating Policy for Reservoirs Using Neural Network , 1996 .

[29]  Li-Chiu Chang,et al.  Intelligent control for modelling of real‐time reservoir operation , 2001 .

[30]  Nachimuthu Karunanithi,et al.  Neural Networks for River Flow Prediction , 1994 .

[31]  Ashish Sharma,et al.  A study of optimal model lag and spatial inputs to artificial neural network for rainfall forecasting , 2000 .

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

[33]  Gerald N. Day,et al.  Extended Streamflow Forecasting Using NWSRFS , 1985 .

[34]  John W. Labadie,et al.  Optimal Operation of Multireservoir Systems: State-of-the-Art Review , 2004 .

[35]  A. Shamseldin.  Application of a neural network technique to rainfall-runoff modelling , 1997 .

[36]  Jan Seibert,et al.  On the need for benchmarks in hydrological modelling , 2001 .

[37]  Holger R. Maier,et al.  Neural networks for the prediction and forecasting of water resource variables: a review of modelling issues and applications , 2000, Environ. Model. Softw..

[38]  Chun-Chieh Yang,et al.  Artificial Neural Network Model for Subsurface-Drained Farmlands , 1997 .

[39]  D. K. Srivastava,et al.  Application of ANN for Reservoir Inflow Prediction and Operation , 1999 .