Modeling Daily Pan Evaporation in Humid Climates Using Gaussian Process Regression

TL;DR

This study compares machine learning models, especially Gaussian Process Regression, to accurately estimate daily pan evaporation in humid Iranian climates using minimal meteorological data.

Contribution

It demonstrates the effectiveness of Gaussian Process Regression over other models for estimating pan evaporation with limited meteorological inputs.

Findings

GPR achieved the highest accuracy among tested models.

Few meteorological parameters can reliably estimate evaporation.

Models performed well across different stations.

Abstract

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.