TITLE:
Application of Soft Computing Methods in Predicting Evapotranspiration
AUTHORS:
Afshin Honarbakhsh, Mostafa Moradi Dashtpagerdi, Hassan Vagharfard
KEYWORDS:
Evapotranspiration; Fuzzy Rule Base; Fuzzy Regression; Artificial Neural Network
JOURNAL NAME:
Open Journal of Geology,
Vol.3 No.7,
November
18,
2013
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.