Dynamic emulations of surface radiation components during day and night under all sky and surface conditions using temporal neural networks

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dc.authorid0000-0003-1099-4363en_US
dc.contributor.authorEvrendilek, Fatih
dc.contributor.authorÇelik, Naci Ali
dc.date.accessioned2021-06-23T19:34:11Z
dc.date.available2021-06-23T19:34:11Z
dc.date.issued2013
dc.departmentBAİBÜ, Mühendislik Fakültesi, Çevre Mühendisliği Bölümüen_US
dc.description.abstractModeling of land surface radiation budget and its components is essential to a better understanding of soil-vegetation-atmosphere interactions. Time Delay Neural Network (TDNN) and Time Lag Recurrent Network (TLRN) models were used to emulate all the hourly surface radiation components for a temperate peatland during day and night under all-sky and -surface conditions. Sensitivity analyses of full versus reduced models, daytime versus nighttime periods, and TDNN versus TLRN models were carried out using training-, cross-validation, and testing-derived metrics of root mean squared error (RMSE), mean absolute error (MAE), and coefficient of determination (R-2) for each of the components. The full daytime temporal neural network models performed best based on RMSE of 2.3 W m(-2) in downwelling longwave radiation to 112.2 W m(-2) in upwelling shortwave radiation; R-2 of 0.52 in downwelling longwave radiation to 0.88 in net shortwave radiation and net radiation; and MAE of 1.73 W m(-2) in downwelling longwave radiation to 90.57 W m(-2) in upwelling shortwave radiation. The best nighttime TDNN models led to RMSE values that ranged from 4.4 W m(-2) in downwelling longwave radiation to 9.3 W m(-2) in upwelling longwave radiation; R-2 values that ranged from 0.38 in net longwave radiation to 0.60 in downwelling longwave radiation; and MAE values that ranged from 4.1 W m(-2) in downwelling longwave radiation to 8.1 W m(-2) in upwelling longwave radiation. Temporal neural networks used in this study appear to be a promising approach to predict nonlinear behaviors of the daytime and nighttime surface radiation components.en_US
dc.identifier.doi10.1080/15435075.2012.732634
dc.identifier.endpage983en_US
dc.identifier.issn1543-5075
dc.identifier.issn1543-5083
dc.identifier.issue9en_US
dc.identifier.scopus2-s2.0-84879221227en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage966en_US
dc.identifier.urihttps://doi.org/10.1080/15435075.2012.732634
dc.identifier.urihttps://hdl.handle.net/20.500.12491/7416
dc.identifier.volume10en_US
dc.identifier.wosWOS:000320183500006en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorEvrendilek, Fatih
dc.institutionauthorÇelik, Naci Ali
dc.language.isoenen_US
dc.publisherTaylor & Francis Incen_US
dc.relation.ispartofInternational Journal Of Green Energyen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectLongwave Radiationen_US
dc.subjectPeatlanden_US
dc.subjectShortwave Radiationen_US
dc.subjectTime Delay Neural Networken_US
dc.subjectTime Lag Recurrent Networken_US
dc.titleDynamic emulations of surface radiation components during day and night under all sky and surface conditions using temporal neural networksen_US
dc.typeArticleen_US

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