Estimation of daily pan evaporation using artificial neural network and multivariate non-linear regression

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 (E (P)) data, the estimation of E (P) for such studies seems necessary. The main aim of this paper was to estimate daily E (P) 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 E (P). 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 E (P). The sensitivity analysis revealed that the estimated E (P) 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 E (P).