Paper Information

Journal:   IRAN-WATER RESOURCES RESEARCH   SPRING 2017 , Volume 13 , Number 1 #M0054; Page(s) 152 To 162.
 
Paper: 

COMBINED APPLICATION OF ARTIFICIAL NEURAL NETWORK AND COMPUTATIONAL METHODS TO ESTIMATE THE REFERENCE EVAPOTRANSPIRATION

 
 
Author(s):  SHABANI A.*, SEPASKHAH A.R., BAHRAMI M., RAZZAGHI F.
 
* WATER SCIENCE AND ENGINEERING DEPARTMENT, FASA UNIVERSITY, IRAN
 
Abstract: 

Estimation of reference evapotranspiration (ETo) is essential for many issues in irrigation and drainage, hydrology, environment, soil erosion, and water resources. Using the artificial neural network (ANN) to estimate ETo is common in lots of studies. But what has not been addressed in previous studies is using the meteorological data as an input of neural network together with computational methods. In this study meteorological data and the ETo calculated by computational methods including Jensen-Haise, Turc, Hargreaves-Samani, and pan evaporation methods were used as input data. Results showed that among all of computational methods using the calculated ETo by Jensen-Haise method together with meteorological data as input data resulted in closer estimation to calculated ETo by Penman-Montieth-FAO. Using the calculated ETo by other methods along with meteorological data as well improved the ETo estimation compared with using the meteorological data lonely. However the accuracy of ETo estimation by using these methods were still low.

 
Keyword(s): ARTIFICIAL NEURAL NETWORK, HARGREAVES-SAMANI, JENSEN-HAISE, PENMAN-MONTIETH-FAO, TURC
 
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