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dc.creatorPierini,Jorge O
dc.creatorGómez,Eduardo A
dc.creatorTelesca,Luciano
dc.date2012-11-01
dc.date.accessioned2019-05-03T13:27:20Z
dc.date.available2019-05-03T13:27:20Z
dc.identifierhttps://scielo.conicyt.cl/scielo.php?script=sci_arttext&pid=S0718-560X2012000400005
dc.identifier.urihttp://revistaschilenas.uchile.cl/handle/2250/87829
dc.descriptionThe identification of suitable models for predicting daily water flow is important for planning and management of water storage in reservoirs of Argentina. Long-term prediction of water flow is crucial for regulating reservoirs and hydroelectric plants, for assessing environmental protection and sustainable development, for guaranteeing correct operation of public water supply in cities like Catriel, 25 de Mayo, Colorado River and potentially also Bahía Blanca. In this paper, we analyze in Buta Ranquil flow time series upstream reservoir and hydroelectric plant in order to model and predict daily fluctuations. We compare results obtained by using a three-layer artificial neural network (ANN), and an autoregressive (AR) model, using 18 years of data, of which the last 3 years are used for model validation by means of the root mean square error (RMSE), and measure of certainty (Skill). Our results point out to the better performance to predict daily water flow or refill them of the ANN model performance respect to the AR model.
dc.formattext/html
dc.languageen
dc.publisherPontificia Universidad Católica de Valparaíso. Facultad de Recursos Naturales. Escuela de Ciencias del Mar
dc.relation103856/vol40-issue4-fulltext-5
dc.rightsinfo:eu-repo/semantics/openAccess
dc.sourceLatin american journal of aquatic research v.40 n.4 2012
dc.subjectprediction
dc.subjecttime series
dc.subjectneural networks
dc.subjectautoregressive models
dc.subjectflows
dc.subjectColorado River
dc.subjectArgentina
dc.titlePrediction of water flows in Colorado River, Argentina


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