Application of Soft Computing Methods in Predicting Evapotranspiration

Afshin Honarbakhsh,Hassan Vagharfard,Mostafa Moradi Dashtpagerdi

doi:10.4236/ojg.2013.37045

Afshin Honarbakhsh, Hassan Vagharfard + Show 1 more

Open Access

https://doi.org/10.4236/ojg.2013.37045

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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.

Highlights

Evapotranspiration is one of the most important factors in agriculture and the hydrological cycle that can be influenced by global warming and climatic changes [1]
Soft Computing Methods in predicting evapotranspiration were reviewed in west of Iran
It is noted that root mean square error (RMSE) and R2 were used for validation and approval of the results

Summary

Introduction

Evapotranspiration is one of the most important factors in agriculture and the hydrological cycle that can be influenced by global warming and climatic changes [1]. The process of evapotranspiration (ET) is an important part of the water cycle and exactly estimating the value of ET is necessary for designing irrigation systems and water resources management. Accurate estimation of ET is crucial in agriculture. This is due to the fact that its over-estimation causes waste of valuable water resources and its underestimation leads to the plant moisture stress and decrease in the crop yield. Montieth et al (1965) later improved this method by considering the plant daily resistance and Penman-Montieth equation [3].

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