Abstract:
Estimation of evapotranspiration (ET) is an
essential component of the hydrologic cycle, which is
also requisite for efficient irrigation water management
planning and hydro-meteorological studies at both the
basin and catchment scales. There are about twenty wellestablished
methods available for ET estimation which
depends upon various meteorological parameters and
assumptions. Most of these methods are physically based
and need a variety of input data. The FAO-56 Penman–
Monteith method (PM) for estimating reference evapotranspiration
(ET0) is recommend for irrigation
scheduling worldwide, because PM generally yields the
best results under various climatic conditions. This study
investigates the abilities of artificial neural networks
(ANN) to improve the accuracy of monthly evaporation
estimation in sub-humid climatic region of Dehradun. In
the first part of the study, different ANN models, comprising
various combinations of training function and
number of neutrons were developed to estimate the ET0
and it has been compared with the Penman–Monteith
(PM) ET0 as the ideal (observed) ET0. Various statistical
approaches were considered to estimate the model performance,
i.e. Coefficient of Correlation (r), Sum of
Squared Errors, Root Mean Square Error, Nash–Sutcliffe
Efficiency Index (NSE) and Mean Absolute Error. The
ANN model with Levenberg–Marquardt training algorithm,
single hidden layer and nine number of neutron
schema was found the best predicting capabilities for the
study station with Coefficient of Correlation (r) and NSE
value of 0.996 and 0.991 for calibration period and 0.990
and 0.980 for validation period, respectively. In the
subsequent part of the study, the trend analysis of ET0
time series revealed a rising trend in the month of
March, and a falling trend during June to November,
except August, with more than 90% significance level
and the annual declining rate was found to 1.49 mm per
year.
