论文信息 - Data preprocessing for river flow forecasting using neural networks: Wavelet transforms and data partitioning

Data preprocessing for river flow forecasting using neural networks: Wavelet transforms and data partitioning

Abstract The evaluation of surface water resources is a necessary input to solving water management problems. Neural network models have been trained to predict monthly runoff for the Tirso basin, located in Sardinia (Italy) at the S. Chiara section. Monthly time series data were available for 69 years and are characterized by non-stationarity and seasonal irregularity, which is typical of a Mediterranean weather regime. This paper investigates the effects of data preprocessing on model performance using continuous and discrete wavelet transforms and data partitioning. The results showed that networks trained with pre-processed data performed better than networks trained on undecomposed, noisy raw signals. In particular, the best results were obtained using the data partitioning technique.

[1] Linda See,et al. Applying soft computing approaches to river level forecasting , 1999 .

[2] Augusto Montisci,et al. Comparing artificial neural networks and support vector machines for modelling rainfall-runoff , 2004 .

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

[4] Jose C. Principe,et al. Neural and adaptive systems , 2000 .

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

[6] 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 .

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

[8] Ingrid Daubechies,et al. Ten Lectures on Wavelets , 1992 .

[9] Stan Openshaw,et al. A hybrid multi-model approach to river level forecasting , 2000 .

[10] M. Lorrai,et al. Neural nets for modelling rainfall-runoff transformations , 1995 .

[11] George Cybenko,et al. Approximation by superpositions of a sigmoidal function , 1992, Math. Control. Signals Syst..

[12] R. V. Sachs,et al. Wavelets in time-series analysis , 1999, Philosophical Transactions of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.