| contributor author | Shalamu Abudu | |
| contributor author | A. Salim Bawazir | |
| contributor author | J. Phillip King | |
| date accessioned | 2017-05-08T21:52:42Z | |
| date available | 2017-05-08T21:52:42Z | |
| date copyright | May 2010 | |
| date issued | 2010 | |
| identifier other | %28asce%29ir%2E1943-4774%2E0000224.pdf | |
| identifier uri | http://yetl.yabesh.ir/yetl/handle/yetl/65084 | |
| description abstract | 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 | |
| publisher | American Society of Civil Engineers | |
| title | Infilling Missing Daily Evapotranspiration Data Using Neural Networks | |
| type | Journal Paper | |
| journal volume | 136 | |
| journal issue | 5 | |
| journal title | Journal of Irrigation and Drainage Engineering | |
| identifier doi | 10.1061/(ASCE)IR.1943-4774.0000197 | |
| tree | Journal of Irrigation and Drainage Engineering:;2010:;Volume ( 136 ):;issue: 005 | |
| contenttype | Fulltext | |
