Estimation of Daily Evaporation Using of Artificial Neural Networks (Case Study; Borujerd Meteorological Station)

Document Type : Research and Full Length Article


1 Islamic Azad University, Boroūjerd Branch, Boroūjerd

2 Imam Khomaini Higher Education Center Agricultural Jehade, Lorestan


Evaporation is one of the most important components of hydrologic cycle.
Accurate estimation of this parameter is used for studies such as water balance,
irrigation system design, and water resource management. In order to estimate the
evaporation, direct measurement methods or physical and empirical models can be
used. Using direct methods require installing meteorological stations and
instruments for measuring evaporation. Installing such instruments in various areas
requires specific facilities and cost which is impossible to be specified. Pan
evaporation is one of the most popular instruments for direct measuring. In this
research, by using daily temperature, relative humidity, wind velocity, sunshine
hours, and evaporation data in meteorological station and neural network model,
daily evaporation is estimated. Network training using daily data takes three years
and network testing takes one year in which data is standardize for training and
testing the model. In this model, a feed forward multiple layer network with a
hidden layer and sigmoid function is used. The results show the suitable capability
and acceptable accuracy of artificial neural networks in estimating of daily
evaporation. Best model for estimation of evaporation is ANN (5-4-1), it have MSE
0.006716 and R2 0.725398. Artificial neural networking is one of the methods for
estimate evaporation. In this method can use in any area that have only maximum
and minimum data for estimate evaporation.


Abtew, W. 2001. Evaporation estimation
for Lake Okeechobee in south florida,
Journal of Irrig 127(3): 40-147.
Amini, M.R, Balarastaghi, E. 2009. An
efficient BFSK neural network joint
equalizer-demodulator. World Academy
of science, Engineering and Technology,
PP. 978-980.
Buton J.M, Mc clendon R.W.
Hoogenboom G. 2000. Estimating daily
pan evaporation with artificial neural
network. American society of
Agricultural Engineers, 43 (2): 491-496.
Karamuz M. and Araghinezhad SH., 2005.
Advanced hydrology. Sannati amirkabir
University Press. Pp. 138-142.
K. P. Sudheer, A. K. Gosain, D. Mohana
Rangan and S. M. Saheb, 2002.
Modeling evaporation using an artificial
neural network algorithm. Hydrol.
Process. 16: 3189-3202
K. P. Sudheer; A. K.Gosain; and K.S.
Ramasastri, 2003. Estimating Actual
Evapotranspiration from Limited
Climatic Data Using Neural Computing
Technique. Journal of Irrigation and
Drainage Engineering, 129 (3): 214.218.
Mahdavi M., 2003. Application hydrology.
University of Tehran Press. PP. 119-168.
(In persian).
M. Erol Keskin and Ozlem Terzi, 2006.
Artificial Neural Network Models of
Daily Pan Evaporation. Journal of
Hydrologic Engineering, 11(1): 65-76.
M. Kumar; N.S. Raghuwanshi; R. Singh;
W. W. Wallender; and W. O. Pruitt, 2002.
Estimating Evapotranspiration using
Artificial Neural Network. Journal of
Irrigation and Dranage Engineering.
128 (4): 224-2330.
Linsley R.K, Kohler M.A. and
Paulhus,J.L.H., 1982. Hydrology for
engineers, McGraw-Hill, London.
Shayannezhad M., 2005. Comparison of
artificial neural networks accuracy and
Penman-Mantis in measurement of
evapotranspiration. National conference
of management of drainage and
irrigation. Shahid Chamran Ahvaz
University, Iran (In persian).
S. Zanetti; E. F. Sousa; V. P. S. Oliveira; F.
T. Almedia; and S. Bernardo, 2007.
Estimating Evapotranspiration Using
Artificial Neural Network and Minimum
Climatological Data. Journal of
Irrigation and Drainage Engineering,
133(2): 83-89.