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- Title
Estimation of daily pan evaporation using artificial neural network and multivariate non-linear regression.
- Authors
Tabari, Hossein; Marofi, Safar; Sabziparvar, Ali-Akbar
- Abstract
Measurement of evaporation ( E) rate from various natural surfaces is known as the key element in any hydrological cycle and hydrometeorological studies. Due to the shortage of pan evaporation ( EP) data, the estimation of EP for such studies seems necessary. The main aim of this paper was to estimate daily EP using artificial neural network (ANN) and multivariate non-linear regression (MNLR) methods in semi-arid region of Iran. Five different ANN and MNLR models comprising various combinations of daily meteorological variables, that is, relative humidity (RH), air temperature ( T), solar radiation (SR), wind speed ( U) and precipitation ( P) were developed to evaluate degree of effect of each of these variables on EP. The comparison of models estimates showed that the ANN 5 model characterized by Delta-Bar-Delta learning algorithm and Sigmoid activation function which uses all input parameters ( T, U, SR, RH, P) performed best in prediction of daily EP. The sensitivity analysis revealed that the estimated EP data are more sensitive to T and U, respectively. A comparison of the model performance between ANN and MNLR models indicated that ANN method presents the best estimates of daily EP.
- Subjects
ARTIFICIAL neural networks; EVAPORATION (Meteorology); HYDROMETEOROLOGICAL cycles; HUMIDITY; REGRESSION analysis
- Publication
Irrigation Science, 2010, Vol 28, Issue 5, p399
- ISSN
0342-7188
- Publication type
Article
- DOI
10.1007/s00271-009-0201-0