Water Science & Technology (2016)
DOI: 10.2166/wst.2016.002
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 of Spain), one, two and three days ahead. Transit data analyses have showed a coefficient of correlation of 0.53 for a lag between precipitation and discharge of one day. By other hand, temperature and discharge has a coefficient of correlation negative (−0.43) for a delay of 19 day. 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 of 16 years later. Results of this testing period also show a good correlation with a 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.
