Modeling of Reference Crop Evapotranspiration in Wet and Dry Climates Using Data-Mining Methods and Empirical Equations

Abstract

In the present study, performance of data-mining methods in modeling and estimating reference crop evapotranspiration (ETo) is investigated. To this end, different machine learning, including Artificial Neural Network (ANN), M5 tree, Multivariate Adaptive Regression Splines (MARS), Least Square Support Vector Machine (LS-SVM), and Random Forest (RF) are employed by considering different criteria including impacts of climate (eight synoptic stations in humid and dry climates), accuracy, uncertainty and computation time. Furthermore, to show the application of data-mining methods, their results are compared with some empirical equations, that indicated the superiority of data- mining methods. In the humid climate, it was demonstrated that M5 tree model is the best if only accuracy criterion is considered, and MARS is a better data-mining method by considering accuracy, uncertainty, and computation time criteria. While in the dry climate, the ANN has better results by considering accuracy and all other criteria. In the final step, for a comprehensive investigation of data-mining ability in ETo modeling, all data in humid and dry climates are combined. Results showed the highest accuracy by MARS and ANN models.

Keywords

• climate
• reference crop evapotranspiration
• data-mining methods
• uncertainty