Comparison of static-feedforward and dynamic-feedback neural networks for rainfall -runoff modeling

A systematic comparison of two basic types of neural network, static and dynamic, is presented in this study. Two backpropagation (BP) learning optimization algorithms, the standard BP and conjugate gradient (CG) method, are used for the static network, and the real-time recurrent learning (RTRL) algorithm is used for the dynamic-feedback network. Twenty-three storm-events, about 1632 rainfall and runoff data sets, of the Lan-Yang River in Taiwan are used to demonstrate the efficiency and practicability of the neural networks for one hour ahead streamflow forecasting. In a comparison of searching algorithms for a static network, the results show that the CG method is superior to the standard BP method in terms of the efficiency and effectiveness of the constructed network’s performance. For a comparison of the static neural network using the CG algorithm with the dynamic neural network using RTRL, the results show that (1) the static-feedforward neural network could produce satisfactory results only when there is a sufficient and adequate training data set, (2) the dynamic neural network generally could produce better and more stable flow forecasting than the static network, and (3) the RTRL algorithm helps to continually update the dynamic network for learning—this feature is especially important for the extraordinary time-varying characteristics of rainfall – runoff processes. q 2004 Elsevier B.V. All rights reserved.

[1]  K. Thirumalaiah,et al.  River Stage Forecasting Using Artificial Neural Networks , 1998 .

[2]  A. Soldati,et al.  River flood forecasting with a neural network model , 1999 .

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

[4]  A. Elshorbagy,et al.  Performance Evaluation of Artificial Neural Networks for Runoff Prediction , 2000 .

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

[6]  Bellie Sivakumar,et al.  River flow forecasting: use of phase-space reconstruction and artificial neural networks approaches , 2002 .

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

[8]  J J Hopfield,et al.  Neural networks and physical systems with emergent collective computational abilities. , 1982, Proceedings of the National Academy of Sciences of the United States of America.

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

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

[11]  Keith Beven,et al.  Dalton Medal Lecture: How far can we go in distributed hydrological modelling? , 2001 .

[12]  A. W. Minns,et al.  Artificial neural networks as rainfall-runoff models , 1996 .

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

[14]  T. Kohonen.  Self-organized formation of topographically correct feature maps , 1982 .

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

[16]  Teuvo Kohonen,et al.  Self-organized formation of topologically correct feature maps , 2004, Biological Cybernetics.

[17]  M. J. D. Powell,et al.  Radial basis functions for multivariable interpolation: a review , 1987 .

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

[19]  J. Eheart,et al.  Neural network-based screening for groundwater reclamation under uncertainty , 1993 .

[20]  R. Abrahart,et al.  Comparing neural network and autoregressive moving average techniques for the provision of continuous river flow forecasts in two contrasting catchments , 2000 .

[21]  B. Bobée,et al.  Multivariate Reservoir Inflow Forecasting Using Temporal Neural Networks , 2001 .

[22]  Jason Smith,et al.  Neural-Network Models of Rainfall-Runoff Process , 1995 .

[23]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

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

[25]  Slobodan P. Simonovic,et al.  Short term streamflow forecasting using artificial neural networks , 1998 .

[26]  R. Golob,et al.  Neural-network-based water inflow forecasting , 1997 .

[27]  B. S. Thandaveswara,et al.  A non-linear rainfall–runoff model using an artificial neural network , 1999 .

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

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

[30]  Christian W. Dawson,et al.  Hydrological modelling using artificial neural networks , 2001 .

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

[32]  W S McCulloch,et al.  A logical calculus of the ideas immanent in nervous activity , 1990, The Philosophy of Artificial Intelligence.

[33]  Sevket Durucan,et al.  River flow prediction using artificial neural networks: generalisation beyond the calibration range. , 2000 .

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

[35]  Yen-Chang Chen,et al.  Flood forecasting using radial basis function neural networks , 2001, IEEE Trans. Syst. Man Cybern. Part C.

[36]  Ronald J. Williams,et al.  A Learning Algorithm for Continually Running Fully Recurrent Neural Networks , 1989, Neural Computation.

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

[38]  Yen-Chang Chen,et al.  Estuary water-stage forecasting by using radial basis function neural network , 2003 .

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

[40]  L. See,et al.  An evaluation of a traditional and a neural net modelling approach to flood forecasting for an upland catchment , 2002 .

[41]  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..