Journal of Petroleum & Environmental Biotechnology

Abstrait

Conceptual Reference Evapotranspiration Models for Different Time Steps

Laaboudi A, Mouhouche B and Draoui B

The evapotranspiration is one of the basic components of the hydrologic cycle and is essential for estimating irrigation water requirements. The use of Artificial Neural Networks (ANNs) in estimation of reference evapotranspiration has received enormous interest in the present decade. This paper describes the results obtained using neural network techniques to improve the accuracy of reference evapotranspiration estimation in different situations. Because the Neural networks are proved to be parsimonious universal approximators of nonlinear function, we have exploited this property to build various models in situation of lack of meteorological parameters and in different time steps. The FAO-56 Penman–Monteith equation (PM) was used to compute the reference evapotranspiration values. The study showed that the neural network technique performed the best models even when it is feared the risk of co linearity and provided the best results by choosing appropriate architecture. They were able to reduce both Root Mean Squared Error and Mean Absolute Relative Error values and at the same time maximize the Nash-Sutcliffe efficiency and coefficient determination values.