Investigating the Performance of Neural Network Based Group Method of Data Handling to Pan's Daily Evaporation Estimation (Case Study: Garmsar City)

Abstract

Evaporation is a complex and nonlinear phenomenon due to the interactions of different climatic factors. Therefore, advanced models should be used to estimate evaporation. In the present study, the Neural Network-Based Group Method of Data Handling was used to estimate and simulate the evaporation rate from the pan in the synoptic station of Garmsar city located in Semnan province, Iran. For this purpose, the daily meteorological data of evaporation, minimum and maximum temperature, wind speed, relative humidity, air pressure, and sunny hours of the said station during the nine years (2009-2018) were used. The percent of data on training, test, number of the used layers, and the highest number of neurons were considered as 60%, 40%, 5%, and 30%, respectively. The studied method's accuracy was investigated using the statistical parameter of Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and correlation coefficient, and. Sensitivity analysis of the input parameters was performed using the GMDH-NN model. This study showed that R2, RMSE, and MAE values in the test phase were obtained as 0.84, 2.65, and 1.91, respectively, in the most optimal state. From the third layer onwards, the amount of the best mean squared errors of the ‎Validation data have converged to 0.062, and it is not affordable to use more layers ‎for the modeling of the evaporation pan in the Garmsar station.‎ The standard deviation and mean amounts of the errors are -0.1210 and 2.552 ‎respectively.‎ The amounts of the best mean squared errors of the validation data are presented. ‎It shows that although the layers are increased, the amounts of the mean squared ‎errors have not changed considerably. (Maximum 0.003). The sensitivity analysis results showed that the two input parameters of minimum temperature and relative humidity percent have a higher effect on evaporation pan modeling than other input parameters.

Keywords

• pan evaporation
• gmdh-nn
• hydrology
• sensitivity analysis
• garmsar