Infilling Missing Daily Evapotranspiration Data Using Neural Networks

Autor:	Shalamu Abudu, A. Salim Bawazir, J. Phillip King
Rok vydání:	2010
Předmět:	Data set Hydrology Gap filling Coefficient of determination Data collection Mean squared error Artificial neural network Evapotranspiration Environmental science Missing data Agricultural and Biological Sciences (miscellaneous) Water Science and Technology Civil and Structural Engineering
Zdroj:	Journal of Irrigation and Drainage Engineering. 136:317-325
ISSN:	1943-4774 0733-9437
Popis:	This study used artificial neural networks (ANNs) computing technique for infilling missing daily saltcedar evapotranspiration (ET) as measured by the eddy-covariance method. The study site was at Bosque del Apache National Wildlife Refuge in the Middle Rio Grande Valley, New Mexico. Data was collected from 2001 to 2003. Several ANN models were evaluated for infilling of different combinations of missing data percentages and different gap sizes. The ANN model using daily maximum and minimum temperature, daily solar radiation, day of the year, and the calendar year as inputs showed the best estimation performance. Results showed coefficient of determination ( R2 ) of 0.96, root-mean-square error (RMSE) of 0.4 mm/day for 10% missing data and a maximum of half-month gap size data set. Missing data greater than 30% and maximum data gap size greater than 3 months resulted in R2 less than 0.90 and RMSE greater than 0.6 mm/day. The results from this study suggest that infilling of daily saltcedar ET using ANN an...
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::ca6fd076fcdfa317ccc935d8f87cd2aa https://doi.org/10.1061/(asce)ir.1943-4774.0000197 Zobrazit plný text záznamu Plný text ve formátu PDF