  Evaporation is one of the main processes in the hydrological cycle, and it is
one of the most critical factors in agricultural, hydrological, and
meteorological studies. Due to the interactions of multiple climatic factors,
the evaporation is a complex and nonlinear phenomenon; therefore, the
data-based methods can be used to have precise estimations of it. In this
regard, in the present study, Gaussian Process Regression, Nearest-Neighbor,
Random Forest and Support Vector Regression were used to estimate the pan
evaporation in the meteorological stations of Golestan Province, Iran. For this
purpose, meteorological data including PE, temperature, relative humidity, wind
speed and sunny hours collected from the Gonbad-e Kavus, Gorgan and Bandar
Torkman stations from 2011 through 2017. The accuracy of the studied methods
was determined using the statistical indices of Root Mean Squared Error,
correlation coefficient and Mean Absolute Error. Furthermore, the Taylor charts
utilized for evaluating the accuracy of the mentioned models. We report that
GPR for Gonbad-e Kavus Station with input parameters of T, W and S and GPR for
Gorgan and Bandar Torkmen stations with input parameters of T, RH, W, and S had
the most accurate performances and proposed for precise estimation of PE. Due
to the high rate of evaporation in Iran and the lack of measurement
instruments, the findings of the current study indicated that the PE values
might be estimated with few easily measured meteorological parameters
accurately.
