Application example of neural networks for time series analysis: : Rainfall-runoff modeling

Abstract The study presented in this paper represents a variable selection method based on the stochastic exploration of the input space referring to the input variables neighborhood. The ANN structure properly instructed to perform the required mapping, had been submitted to the known input variables, but contaminated with noise purposefully to reduce the initial number of used variables through the determination of the relevancy of each variable initially used. The proposed method is comparatively analyzed with standard stepwise regression method widely used in the variable selection procedure. The proposed method showed better performances in comparison to stepwise regression method. The problem associated with this method may be the time required for the high dimensional input space exploration.

[1]  A. Hodgkin,et al.  Action Potentials Recorded from Inside a Nerve Fibre , 1939, Nature.

[2]  R. R. Hocking The analysis and selection of variables in linear regression , 1976 .

[3]  Ken-ichi Funahashi,et al.  On the approximate realization of continuous mappings by neural networks , 1989, Neural Networks.

[4]  Kenneth W. Bauer,et al.  Improved feature screening in feedforward neural networks , 1996, Neurocomputing.

[5]  S. Sorooshian,et al.  Effective and efficient global optimization for conceptual rainfall‐runoff models , 1992 .

[6]  Gwilym M. Jenkins,et al.  Time series analysis, forecasting and control , 1971 .

[7]  Y.-Y. Hsu,et al.  Short term load forecasting using a multilayer neural network with an adaptive learning algorithm , 1992 .

[8]  D. Lowe,et al.  Time series prediction by adaptive networks: a dynamical systems perspective , 1991 .

[9]  L. L. Rogers,et al.  Optimization of groundwater remediation using artificial neural networks with parallel solute transport modeling , 1994 .

[10]  Bernard Widrow,et al.  Layered neural nets for pattern recognition , 1988, IEEE Trans. Acoust. Speech Signal Process..

[11]  R.P. Lippmann,et al.  Pattern classification using neural networks , 1989, IEEE Communications Magazine.

[12]  Ehud D. Karnin,et al.  A simple procedure for pruning back-propagation trained neural networks , 1990, IEEE Trans. Neural Networks.

[13]  Roland K. Price,et al.  A neural network model of rainfall-runoff using radial basis functions , 1996 .

[14]  I. Rodríguez‐Iturbe,et al.  Random Functions and Hydrology , 1984 .

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

[16]  D. Rizzo,et al.  Characterization of aquifer properties using artificial neural networks: Neural kriging , 1994 .

[17]  Kumpati S. Narendra,et al.  Identification and control of dynamical systems using neural networks , 1990, IEEE Trans. Neural Networks.

[18]  Richard O. Duda,et al.  Pattern classification and scene analysis , 1974, A Wiley-Interscience publication.

[19]  Richard S. Sutton,et al.  Neuronlike adaptive elements that can solve difficult learning control problems , 1983, IEEE Transactions on Systems, Man, and Cybernetics.

[20]  Y.-Y. Hsu,et al.  Design of artificial neural networks for short - term load forecasting , 1991 .

[21]  Teuvo Kohonen,et al.  Self-organization and associative memory: 3rd edition , 1989 .

[22]  G. Moschytz,et al.  Selecting inputs and measuring nonlinearity in system identification , 1996, Proceedings of International Workshop on Neural Networks for Identification, Control, Robotics and Signal/Image Processing.

[23]  Tommy W. S. Chow,et al.  Neural network based short-term load forecasting using weather compensation , 1996 .

[24]  Y. Shimakura,et al.  Short-term load forecasting using an artificial neural network , 1993, [1993] Proceedings of the Second International Forum on Applications of Neural Networks to Power Systems.

[25]  Russell Reed,et al.  Pruning algorithms-a survey , 1993, IEEE Trans. Neural Networks.

[26]  George E. P. Box,et al.  Time Series Analysis: Forecasting and Control , 1977 .

[27]  E. Capaldi,et al.  The organization of behavior. , 1992, Journal of applied behavior analysis.

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

[29]  Vijay P. Singh,et al.  Hydrologic Systems: Rainfall-Runoff Modeling , 1988 .

[30]  Calyampudi R. Rao Handbook of statistics , 1980 .

[31]  Marie Cottrell,et al.  Neural modeling for time series: A statistical stepwise method for weight elimination , 1995, IEEE Trans. Neural Networks.

[32]  Yuan-Yih Hsu,et al.  Design of artificial neural networks for short-term load forecasting. I. Self-organising feature maps for day type identification , 1991 .

[33]  P. Lisboa,et al.  Sensitivity methods for variable selection using the MLP , 1996, Proceedings of International Workshop on Neural Networks for Identification, Control, Robotics and Signal/Image Processing.

[34]  Regina Lamedica,et al.  A neural network based technique for short-term forecasting of anomalous load periods , 1996 .