Sensitivity Analysis of ANN Inputs in Estimating Daily Evaporation

Estimation of evaporation values is needed for efficient management of water resources at semi-arid regions. This paper presents application of Artificial Neural Networks (ANNs), Multiple Linear Regression (MLR) and empirical models viz.: Energy balance، Aerodynamic، Penman for estimation of daily pan evaporation for Tabriz and Urmia cities. Furthermore, in order to determine the effect of each input parameter on the output variable in terms of magnitude and direction and also identify the best combinations of the model inputs, two sensitivity analysis methods i. e. the Partial Derivation method (PaD) and the Weights method have been applied on the ANNs results. The used hydrological variables include daily observations of air temperature, pan evaporation, solar radiation, air pressure, relative humidity, and wind speed. The results of the classic methods and ANN models are compared to daily observations of evaporation values. The comparison showed that there is better agreement between the ANN estimations and measurements of daily evaporation than other models. Sensitivity analysis results showed that air temperature, solar radiation and previous day evaporation have maximum effects on daily evaporation in both regions and the contributions of the other variables are insignificant.