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popularly known as CIMIS Penman equation [ 94 ]. ASCE-PM is a standardised
calculation of reference evapotranspiration (ET) as recommended by the Task
Committee on Standardization of Reference Evapotranspiration of the Environ-
mental and Water Resources Institute of the American Society of Civil Engineers.
Alexandris and Kerkides [ 5 , 6 ] developed a new empirical equation for the hourly
and daily estimation of evapotranspiration, using a limited number of readily
available weather parameters and demonstrated the estimation of hourly values of
ET 0 with a satisfactory degree of accuracy compared with the ASCE-PM estima-
tion. The proposed equation is based on solar radiation, air temperature and relative
humidity. The experiments had been conducted in an experimental
field of The
Agricultural University of Athens (Copais) in central Greece, using surface poly-
nomial regression analysis. Thereafter the model was named the
for ET estimation. Even though, many equations have been developed and adapted
for various applications based on available input data, there are still considerable
amounts of uncertainty existing among engineers and environmental managers as to
which method is to be adopted effectively in the calculation of ET 0 [ 7 ]. Several
studies have been conducted by researchers for comparative evaluation of the most
widely used and strongly recommended models for estimating hourly ET 0 like
Penman
Copais approach
Monteith), CIMIS version of Penman (CI-
MIS-Penman), and the American Society of Civil Engineers version of Penman
-
Monteith (FAO56-Penman
-
-
Monteith (ASCE-PM) [ 5 , 28 , 45 ]. In recent years several papers have evaluated
hourly ET 0 equations (FAO-56 and ASCE Penman
-
Monteith, CIMIS Penman and
Hargreaves) by comparing them with lysimetric measurements [ 15 , 60 ]. Alexandris
and Kerkides [ 5 , 6 ] compared their model (Copais approach) performance with that
of FAO-PM, ASCE-PM and CIMIS-PM for hourly and daily values ET 0 estimation
using statistics and scatter plots.
Later data based approaches, including arti
-
cial intelligent techniques, have
been applied in evapotranspiration estimation. Just as in the case of rainfall runoff
modelling, ANN offered a promising alternative for modelling evapotranspiration in
the case of data scarcity [ 53 , 57 ]. The study by Sudheer et al. [ 92 ] used radial basis
ANN in modelling ET 0 with limited climatic data. The study by Kumar et al. [ 57 ]
used a multilayer perceptron (MLP) with back propagation training algorithm for
estimation of ET 0 utilising various ANN architectures in data limited situations.
Kisi [ 53 ] investigated the estimation of ET 0 using MLP. The results were compared
with the above mentioned traditional approaches like Penman and Hargreaves
'
empirical models. Adaptive Neuro-fuzzy system (ANFIS) has been applied to
evapotranspiration estimation by Kisi and
rk [ 54 ] to check the prediction
capability. Wang and Luo [ 101 ] adopted Wavelet network model for reference crop
evapotranspiration forecasting. A detailed study by El-Sha
Ö
zt
ü
e et al. [ 31 ] suggests
that ANN model is better than ARMA model for multi-lead ahead prediction of
evapotranspiration.
 
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