Evaluating the intelligence models performance in estimation of dew point temperature using meteorological parameters

Dew point temperature is the temperature to which under constant pressure, air becomes saturated with water vapor. The goal of the present research is to evaluate the capability of Artificial Neural Networks (ANN (and Multivariate Adaptive Regression Splines (MARS) for estimating the dew point temperature using meteorological parameters in Khoy synoptic station located in northwest of Iran. Used meteorological data were including maximum air temperature (Tmax), minimum air temperature (Tmin), mean air temperature (T), relative humidity (RH), maximum relative humidity (RHmax), minimum relative humidity (RHmin), solar radiation (S), wind speed (W), station atmospheric pressure (Pa), actual vapor pressure (ea) and saturate vapor pressure (es). The mentioned parameters were entered to the used models with various combinations as inputs. To assess the models outputs results, root mean square error (RMSE), mean absolute error (MAE) and coefficient of determination (R2) were employed. On the basis of the obtained results, the actual vapor pressure (ea) and minimum temperature (Tmin) were the most effective parameters in estimating dew point temperature. Also, the results showed that two used models have adequate accurate to estimate dew point temperature using meteorological parameters. However, the MARS had better performance than ANN in estimating dew point temperature. In general, among the used models and parameters, the MARS with single input of the actual vapor pressure and RMSE= 0.343ºcMAE= 0.480 ºcو R2 =0.991, results the best estimation for of dew point temperature in the test state.