Application of Soft Computing Methods in Predicting Evapotranspiration

Afshin Honarbakhsh, Mostafa Moradi Dashtpagerdi, Hassan Vagharfard

Faculty of Natural Resources and Earth Sciences, University of Shahrekord, Shahrekord, Iran.
Faculty of Natural Resources, Hormozgan University, Bandarabbas, Iran.
Graduated Watershed Management, Organization of Natural Resources and Watershed Management, Karoon Watershed Management Office (KWMO), Shahrekord, Iran.

DOI: 10.4236/ojg.2013.37045                

Abstract

Exact prediction of evapotranspiration is necessary for study, design and management of irrigation systems. In this research, the suitability of soft computing approaches namely, fuzzy rule base, fuzzy regression and artificial neural networks for estimation of daily evapotranspiration has been examined and the results are compared to real data measured by lysimeter on the basis of reference crop (grass). Using daily climatic data from Haji Abad station in Hormozgan, west of Iran, including maximum and minimum temperatures, maximum and minimum relative humidities, wind speed and sunny hours, evapotranspiration was predicted by soft computing methods. The predicted evapotranspiration values from fuzzy rule base, fuzzy linear regression and artificial neural networks show root mean square error (RMSE) of 0.75, 0.79 and 0.81 mm/day and coefficient of determination of (R²) of 0.90, 0.87 and 0.85, respectively. Therefore, fuzzy rule base approach was found to be the most appropriate method employed for estimating evapotranspiration.

Keywords

Evapotranspiration; Fuzzy Rule Base; Fuzzy Regression; Artificial Neural Network

Conflicts of Interest

The authors declare no conflicts of interest.

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