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dc.contributor.author Laqui, Wilber
dc.contributor.author Zubieta Barragán, Ricardo
dc.contributor.author Rau, Pedro
dc.contributor.author Mejía, Abel
dc.contributor.author Lavado, Waldo
dc.contributor.author Ingol, Eusebio
dc.coverage.spatial Perú
dc.date.accessioned 2019-10-02T11:16:44Z
dc.date.available 2019-10-02T11:16:44Z
dc.date.issued 2019
dc.identifier.citation Laqui, W., Zubieta, R., Rau, P., Mejía, A., Lavado, W. & Ingol, E. (2019). Can artificial neural networks estimate potential evapotranspiration in Peruvian highlands?.==$Modeling Earth Systems and Environment, 5,$==1911–1924. https://doi.org/10.1007/s40808-019-00647-2 es_ES
dc.identifier.govdoc index-oti2018
dc.identifier.uri http://hdl.handle.net/20.500.12816/4709
dc.description.abstract Evapotranspiration (ETo) is one of the most important variables of the water cycle when water requirements for irrigation, water resource planning or hydrological applications are analyzed. In this context, models based on artificial neural networks (ANN) of the retro-propagation type can be an alternative method to estimate ETo in highland regions using a number of input variables limited. The objective of this study is to develop ANN models to estimate ETo for the Peruvian highlands using input variables such as maximum air temperature (Tmax), minimum air temperature (Tmin), hours of sunshine (Sh), relative humidity (Rh) and wind speed (Wv), as an alternative method to FAO Penman–Monteith method (FAO-PM56) and Hargreaves–Samani (HS). Daily climatic datasets recorded at 12 meteorological stations between 1963 and 2015 were selected in this study. For evaluation reason, the ETo calculated using the FAO-PM56 was also considered. The main input variable to ANN modeling is Tmax, followed by Sh and Wv or combinations between them. Hargreaves–Samani (HS) showed a poor performance in the estimation of the ETo in the Peruvian highlands compared to the 13 ANN models. Additionally, it was determined that in stations with lower thermal amplitude (< 14.2 °C) the lowest performance levels are presented in the estimation of the ETo with HS equation, which does not occur markedly with the ANN models that they estimate adequately ETo. Therefore, ANN models represent a great option to replace the FAO-PM56 and HS method, when ETo data series are scarce. es_ES
dc.format application/pdf es_ES
dc.language.iso eng es_ES
dc.publisher Springer Link es_ES
dc.relation.ispartof urn:issn:2363-6203
dc.rights info:eu-repo/semantics/closedAccess es_ES
dc.subject Highlands es_ES
dc.subject Artificial intelligence es_ES
dc.subject ETo es_ES
dc.subject Water requirement es_ES
dc.title Can artificial neural networks estimate potential evapotranspiration in Peruvian highlands? es_ES
dc.type info:eu-repo/semantics/article es_ES
dc.subject.ocde http://purl.org/pe-repo/ocde/ford#1.05.00 es_ES
dc.subject.ocde http://purl.org/pe-repo/ocde/ford#1.05.09 es_ES
dc.subject.ocde http://purl.org/pe-repo/ocde/ford#1.05.11 es_ES
dc.identifier.journal Modeling Earth Systems and Environment es_ES
dc.description.peer-review Por pares es_ES
dc.identifier.doi https://doi.org/10.1007/s40808-019-00647-2 es_ES

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