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dc.contributor.authorVega García, Cristina
dc.contributor.authorDecuyper, Mathieu
dc.contributor.authorAlcázar Montero, Jorge
dc.date.accessioned2019-11-15T09:50:54Z
dc.date.available2019-11-15T09:50:54Z
dc.date.issued2019-08-15
dc.identifier.issn2073-4441
dc.identifier.urihttp://hdl.handle.net/10459.1/67548
dc.description.abstractThe analyses of water resources availability and impacts are based on the study over time of meteorological and hydrological data trends. In order to perform those analyses properly, long records of continuous and reliable data are needed, but they are seldom available. Lack of records as in gaps or discontinuities in data series and quality issues are two of the main problems more often found in databases used for climate studies and water resources management. Flow data series from gauging stations are not an exception. Over the last 20 years, forecasting models based on artificial neural networks (ANNs) have been increasingly applied in many fields of natural resources, including hydrology. This paper discusses results obtained on the application of cascade-correlation ANN models to predict daily water flow using Julian day and rainfall data provided by nearby weather stations in the Ebro river watershed (Northeast Spain). Five unaltered gauging stations showing a rainfall-dominated hydrological regime were selected for the study. Daily flow and weather data series covered 30 years to encompass the high variability of Mediterranean environments. Models were then applied to the in-filling of existing gaps under different conditions related to the characteristics of the gaps (6 scenarios). Results showed that when short periods before and after the gap are considered, this is a useful approach, although no general rule applied to all stations and gaps investigated. Models for low-water-flow periods provided better results (r = 0.76–0.8).ca_ES
dc.language.isoengca_ES
dc.publisherMDPIca_ES
dc.relation.isformatofReproducció del document publicat a: https://doi.org/10.3390/w11081691ca_ES
dc.relation.ispartofWater, 2019, vol. 11, num. 8, article number 1691ca_ES
dc.rightscc-by (c) Vega García et al., 2019ca_ES
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectArtificial neural networksca_ES
dc.subjectWater flow modelca_ES
dc.subjectHydrological dataca_ES
dc.subjectData gapsca_ES
dc.titleApplying cascade-correlation neural networks to in-fill gaps in Mediterranean daily flow data seriesca_ES
dc.typeinfo:eu-repo/semantics/articleca_ES
dc.identifier.idgrec029059
dc.type.versioninfo:eu-repo/semantics/publishedVersionca_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessca_ES
dc.identifier.doihttps://doi.org/10.3390/w11081691


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cc-by (c) Vega García et al., 2019
Except where otherwise noted, this item's license is described as cc-by (c) Vega García et al., 2019