Estimation of Daily Evaporation Using of Artificial Neural Networks (Case Study; Borujerd Meteorological Station)

Evaporation is one of the most important components of hydrologic cycle. Accurate estimation of this parameter is used for studies such as water balance, irrigation system design, and water resource management. In order to estimate the evaporation, direct measurement methods or physical and empirical models can be used. Using direct methods require installing meteorological stations and instruments for measuring evaporation. Installing such instruments in various areas requires specific facilities and cost which is impossible to be specified. Pan evaporation is one of the most popular instruments for direct measuring. In this research, by using daily temperature, relative humidity, wind velocity, sunshine hours, and evaporation data in meteorological station and neural network model, daily evaporation is estimated. Network training using daily data takes three years and network testing takes one year in which data is standardize for training and testing the model. In this model, a feed forward multiple layer network with a hidden layer and sigmoid function is used. The results show the suitable capability and acceptable accuracy of artificial neural networks in estimating of daily evaporation. Best model for estimation of evaporation is ANN (5-4-1), it have MSE 0.006716 and R2 0.725398. Artificial neural networking is one of the methods for estimate evaporation. In this method can use in any area that have only maximum and minimum data for estimate evaporation.