A non-linear neural network technique for updating of river flow forecasts

Abstract. A non-linear Auto-Regressive Exogenous-input model (NARXM) river flow forecasting output-updating procedure is presented. This updating procedure is based on the structure of a multi-layer neural network. The NARXM-neural network updating procedure is tested using the daily discharge forecasts of the soil moisture accounting and routing (SMAR) conceptual model operating on five catchments having different climatic conditions. The performance of the NARXM-neural network updating procedure is compared with that of the linear Auto-Regressive Exogenous-input (ARXM) model updating procedure, the latter being a generalisation of the widely used Auto-Regressive (AR) model forecast error updating procedure. The results of the comparison indicate that the NARXM procedure performs better than the ARXM procedure. Keywords: Auto-Regressive Exogenous-input model, neural network, output-updating procedure, soil moisture accounting and routing (SMAR) model

[1]  Kieran M. O'Connor,et al.  A simple non-linear rainfall-runoff model with a variable gain factor , 1994 .

[2]  D. Hammerstrom,et al.  Working with neural networks , 1993, IEEE Spectrum.

[3]  Dingli Yu,et al.  Neural model input selection for a MIMO chemical process , 2000 .

[4]  E. Michael Azoff,et al.  Neural Network Time Series: Forecasting of Financial Markets , 1994 .

[5]  Drainage Division,et al.  Criteria for Evaluation of Watershed Models , 1993 .

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

[7]  J. E. Nash,et al.  The form of the instantaneous unit hydrograph , 1957 .

[8]  Anthony J. Jakeman,et al.  Performance of conceptual rainfall‐runoff models in low‐yielding ephemeral catchments , 1997 .

[9]  Q. J. Wang.  The Genetic Algorithm and Its Application to Calibrating Conceptual Rainfall-Runoff Models , 1991 .

[10]  J. Nash,et al.  River flow forecasting through conceptual models part I — A discussion of principles☆ , 1970 .

[11]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[12]  Viale Risorgimento,et al.  Comparing several genetic algorithm schemes for the calibration of conceptual rainfall-runoff models , 1997 .

[13]  William H. Press,et al.  Numerical recipes , 1990 .

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

[15]  R. K. Kachroo,et al.  River flow forecasting. Part 5. Applications of a conceptual model , 1992 .

[16]  T. V. Hromadka.  A unit hydrograph rainfall-runoff model using Mathematica , 2000, Environ. Model. Softw..

[17]  Marco Lovera,et al.  Identification of the rainfall-runoff relationship in urban drainage networks , 1999 .

[18]  A. Shamseldin,et al.  A real-time combination method for the outputs of different rainfall-runoff models , 1999 .

[19]  Jens Christian Refsgaard,et al.  Validation and Intercomparison of Different Updating Procedures for Real-Time Forecasting , 1997 .

[20]  E. Todini.  Rainfall-runoff modeling — Past, present and future , 1988 .

[21]  A. Shamseldin,et al.  Methods for combining the outputs of different rainfall–runoff models , 1997 .

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

[23]  H. H. Rosenbrock,et al.  An Automatic Method for Finding the Greatest or Least Value of a Function , 1960, Comput. J..

[24]  S. Uhlenbrook,et al.  Multiscale calibration and validation of a conceptual rainfall-runoff model , 2000 .

[25]  N. T. Lange,et al.  New mathematical approaches in hydrological modeling — an application of artificial neural networks , 1999 .

[26]  A. Shamseldin,et al.  Real-Time Flood Forecasting on the Blue Nile River , 1999 .

[27]  Keith Beven,et al.  Discharge and water table predictions using a generalised TOPMODEL formulation , 1997 .

[28]  Claudio Margottini,et al.  Floods and Landslides: Integrated Risk Assessment , 1999 .

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

[30]  Unit hydrographs to model quick and slow runoff components of streamflow , 2000 .

[31]  Laurene V. Fausett,et al.  Fundamentals Of Neural Networks , 1994 .

[32]  A. Becker,et al.  Hydrological models for water-resources system design and operation , 1990 .

[33]  John A. Nelder,et al.  A Simplex Method for Function Minimization , 1965, Comput. J..

[34]  Paul J. Werbos,et al.  Backpropagation Through Time: What It Does and How to Do It , 1990, Proc. IEEE.

[35]  George Kuczera,et al.  Monte Carlo assessment of parameter uncertainty in conceptual catchment models: the Metropolis algorithm , 1998 .

[36]  George Kuczera,et al.  The quest for more powerful validation of conceptual catchment models , 1997 .

[37]  S. Hyakin,et al.  Neural Networks: A Comprehensive Foundation , 1994 .

[38]  Enoch M. Dlamini,et al.  Effects of model complexity and structure, data quality, and objective functions on hydrologic modeling , 1997 .

[39]  P. E. O'Connell,et al.  River flow forecasting through conceptual models part II - The Brosna catchment at Ferbane , 1970 .

[40]  George Kuczera,et al.  Probabilistic optimization for conceptual rainfall-runoff models: A comparison of the shuffled complex evolution and simulated annealing algorithms , 1999 .

[41]  S. Sorooshian,et al.  Automatic calibration of conceptual rainfall-runoff models: sensitivity to calibration data , 1996 .

[42]  Nikola Kasabov,et al.  Foundations Of Neural Networks, Fuzzy Systems, And Knowledge Engineering [Books in Brief] , 1996, IEEE Transactions on Neural Networks.

[43]  V. Klemeš,et al.  A hydrological perspective , 1988 .

[44]  David M. Skapura,et al.  Building neural networks , 1995 .

[45]  Marco Franchini,et al.  Comparing several genetic algorithm schemes for the calibration of conceptual rainfall-runoff models , 1997 .

[46]  Dale E. Seborg,et al.  Determination of model order for NARX models directly from input-output data , 1998 .

[47]  Gail M. Brion,et al.  A neural network approach to identifying non-point sources of microbial contamination , 1999 .

[48]  J. Nicell,et al.  Evaluation of global optimization methods for conceptual rainfall-runoff model calibration , 1997 .

[49]  Keith Beven,et al.  Effects of spatial variability and scale with implications to hydrologic modeling , 1988 .

[50]  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..

[51]  Larry R. Medsker,et al.  Hybrid Neural Network and Expert Systems , 1994, Springer US.

[52]  Bryson C. Bates,et al.  Calibration of a modified SFB model for twenty-five Australian catchments using simulated annealing , 1997 .

[53]  H. Maier,et al.  The Use of Artificial Neural Networks for the Prediction of Water Quality Parameters , 1996 .

[54]  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 .

[55]  H. Houghton-Carr.  Assessment criteria for simple conceptual daily rainfall-runoff models , 1999 .

[56]  R. Lippmann,et al.  An introduction to computing with neural nets , 1987, IEEE ASSP Magazine.

[57]  A. Jayawardena,et al.  A modified spatial soil moisture storage capacity distribution curve for the Xinanjiang model , 2000 .