title: Application of transit data analysis and artificial neural network in the prediction of discharge of Lor River, NW Spain.
abstract: 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 R(2) 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 R(2) 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.
authors.affiliation: Physical Chemistry Department, Faculty of Science, University of Vigo, Ourense 32004, Spain; Department of Geological Sciences, College of Arts and Sciences, Ohio University, Athens, OH 45701, USA.
authors.forename: G
authors.initials: G
authors.lastname: Astray
authors.affiliation: Department of Plant Biology and Soil Science, Faculty of Science, University of Vigo, Ourense 32004, Spain E-mail: edbene@uvigo.es.
authors.forename: B
authors.initials: B
authors.lastname: Soto
authors.affiliation: Department of Geological Sciences, College of Arts and Sciences, Ohio University, Athens, OH 45701, USA.
authors.forename: D
authors.initials: D
authors.lastname: Lopez
authors.affiliation: Physical Chemistry Department, Faculty of Science, University of Vigo, Ourense 32004, Spain.
authors.forename: M A
authors.initials: MA
authors.lastname: Iglesias
authors.affiliation: Physical Chemistry Department, Faculty of Science, University of Vigo, Ourense 32004, Spain.
authors.forename: J C
authors.initials: JC
authors.lastname: Mejuto
mesh_terms: D062070:Hydrology; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D045483:Rivers / Q000737:chemistry*; D013030:Spain; D014874:Water Pollutants, Chemical / Q000737:chemistry*
chemical_list: D014874:Water Pollutants, Chemical