Estimation of Monthly Reference Evapotranspiration Using Regression Tree in Different Climatic Regions of Iran

Document Type : Research Paper

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Abstract

Evapotranspiration is one of the main components of hydrologic cycle and its data is needed to determine the irrigation demand. Artificial intelligence system has been widely used to estimate the hydrological events during the recent decades. The aim of this research was to use the regression tree method  to estimate the reference evapotranspiration (ETo) and to compare with FAO-Penman-Monteith method in different climatic condition across Iran. One of advantages of the Regression Tree model compared to other intelligent models like Neural Networks is that it lacks the time-consuming process of trial-and-error; and representing the results mathematically. Different data such as monthly minimum, average and maximum temperature, relative humidity, wind speed, and solar radiation were used as input to the model. Finally, the results showed that regression tree model can estimate the reference evapotranspiration for different climatic conditions including arid to semi-arid, temperate, and cold climate conditions with 0.78, 0.8,  and 0.89 correlation coefficients, respectively.

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Journal of Water Research in Agriculture
Volume 27, Issue 4
April 2014
Pages 523-534

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