Simulation of Pan Evaporation Rate by ANN Artificial Intelligence Model in Damghan Region

Document Type : Regular Article


1 Faculty of Civil Engineering, Semnan University, Semnan, Iran

2 Professor, Faculty of Civil Engineering, Semnan University, Semnan, Iran


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, average relative humidity, wind speed, sunshine hours, and evaporation during the statistical time period of 16 years (2002-2018). Also, the artificial neural network 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 ANN model, seven different scenarios were considered. About 70 and 30 percentage of the data were used for training and testing, respectively. The model was analyzed by appropriate statistics such as coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE). Results showed that the seventh scenario including minimum and maximum temperatures, average relative humidity, wind speed, ‎sunshine hours‎, and pressure proved to be the superior scenario among others. The values of R2, RMSE, and MAE for the superior scenario were 0.8030, 2.75 mm/day, and 1.88 mm/day, respectively.


Main Subjects

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