Application of Generalized Regression Neural Networks to Intermittent Flow Forecasting and Estimation

The majority of artificial neural network (ANN) applications to water resources data employ the feed-forward back-propagation (FFBP) method. This study used an ANN algorithm, the generalized regression neural network (GRNN), for intermittent river flow forecasting and estimation. GRNNs were superior to FFBP in terms of the selected performance criteria. The GRNN simulations do not face the frequently encountered local minima problem in FFBP applications, and GRNNs do not generate forecasts or estimates that are not physically plausible. Preliminary analysis of statistics such as auto- and cross correlation, which explained variance by multilinear regression and the Akaike criterion for the autoregressive moving average (ARMA) model of corresponding order, were found quite informative in determining the number of nodes in the input layer of neural networks.

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

[2]  H. K. Cigizoglu,et al.  Incorporation of ARMA models into flow forecasting by artificial neural networks , 2003 .

[3]  Andrew Metcalfe,et al.  Bivariate stochastic modelling of ephemeral streamflow , 2002 .

[4]  H. K. Cigizoglu,et al.  ESTIMATION AND FORECASTING OF DAILY SUSPENDED SEDIMENT DATA BY MULTI-LAYER PERCEPTRONS , 2004 .

[5]  Hikmet Kerem Cigizoglu,et al.  Rainfall-Runoff Modelling Using Three Neural Network Methods , 2004, ICAISC.

[6]  Ozgur Kisi,et al.  Flow prediction by three back propagation techniques using k-fold partitioning of neural network training data , 2005 .

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

[8]  Mohammad Bagher Menhaj,et al.  Training feedforward networks with the Marquardt algorithm , 1994, IEEE Trans. Neural Networks.

[9]  Russell C. Eberhart,et al.  Neural network PC tools: a practical guide , 1990 .

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

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

[12]  Slobodan P. Simonovic,et al.  Estimation of missing streamflow data using principles of chaos theory , 2002 .

[13]  W. T. Miller,et al.  CMAC: an associative neural network alternative to backpropagation , 1990, Proc. IEEE.

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

[15]  Hikmet Kerem Cigizoglu.  Discussion of “Performance of Neural Networks in Daily Streamflow Forecasting” by S. Birikundavyi, R. Labib, H. T. Trung, and J. Rousselle , 2004 .

[16]  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..

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

[18]  Valerie Isham,et al.  Some models for rainfall based on stochastic point processes , 1987, Proceedings of the Royal Society of London. A. Mathematical and Physical Sciences.

[19]  Donald F. Specht,et al.  A general regression neural network , 1991, IEEE Trans. Neural Networks.

[20]  A. W. Jayawardena,et al.  Runoff Forecasting Using RBF Networks with OLS Algorithm , 1998 .

[21]  A. Tokar,et al.  Rainfall-Runoff Modeling Using Artificial Neural Networks , 1999 .

[22]  Hikmet Kerem Cigizoglu,et al.  Estimation, forecasting and extrapolation of river flows by artificial neural networks , 2003 .