Data preprocessing for river flow forecasting using neural networks: Wavelet transforms and data partitioning |
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| Authors: | Barbara Cannas Alessandra Fanni Linda See Giuliana Sias |
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| Institution: | a Department of Electrical and Electronic Engineering, University of Cagliari, Cagliari, Italy
b School of Geography, University of Leeds, Leeds, United Kingdom |
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| 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. |
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| Keywords: | Water management Runoff forecasting Neural networks Data preprocessing |
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