Transit data analysis and artificial neural networks (ANNs) have proven to be a useful tool for characterizing and modelling non-linear hydrological processes. In this paper, these methods have been used to characterize and to predict the discharge of Lor River (North Western Spain), 1, 2 and 3 days ahead. Transit data analyses show a coefficient of correlation of 0.53 for a lag between precipitation and discharge of 1 day. On the other hand, temperature and discharge has a negative coefficient of correlation (−0.43) for a delay of 19 days. The ANNs developed provide a good result for the validation period, with R2 between 0.92 and 0.80. Furthermore, these prediction models have been tested with discharge data from a period 16 years later. Results of this testing period also show a good correlation, with R2 between 0.91 and 0.64. Overall, results indicate that ANNs are a good tool to predict river discharge with a small number of input variables.
Application of transit data analysis and artificial neural network in the prediction of discharge of Lor River, NW Spain
G. Astray, B. Soto, D. Lopez, M. A. Iglesias, J. C. Mejuto; Application of transit data analysis and artificial neural network in the prediction of discharge of Lor River, NW Spain. Water Sci Technol 7 April 2016; 73 (7): 1756–1767. doi: https://doi.org/10.2166/wst.2016.002
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