Document Type : Regular Article
Faculty of Civil Engineering, Semnan University, Semnan, Iran
Professor, Civil Engineering Faculty, Semnan University
Regarding different aspects of management of drainage basins and droughts, prediction of evaporation is very important. Evaporation is an essential part of the water cycle and plays an important role in the evaluation of climatic characteristics of any region. The purpose of this research is to predict daily pan evaporation rate of Damghan city using an artificial neural network model. The data applied in this research are daily minimum and maximum temperatures (Tmin and Tmax), average relative humidity (RHmean), wind speed (WS), sunny hours (n), air pressure (PA) and evaporation during the statistical time period of 16 years (2002-2018). Also, the multilayer perceptron was used as a non-linear method to simulate evaporation. Since the units of the inputs and outputs of the prediction model were different, all the data were normalized. In the multilayer perceptron model, 7 different scenarios were considered. About 70 and 30 percentage of the data were used for training and testing, respectively. The model was analyzed using appropriate statistics such as mean square error (RMSE), coefficient of determination (R2),mean absolute error (MAE) and mean square error (MSE). Results showed that the seventh scenario including Tmin, Tmax, RHmean, WS, n , and PA proved to be the superior scenario among others. The values of RMSE, R2, MAE and MSE for the superior scenario were 2.75 mm/day, 0.8030, 1.88 mm/day and (mm/day)2, respectively.