Modelling of Reference Evapotranspiration for Semi-arid Climates Using Artificial Neural Network (original) (raw)

Reference Evapotranspiration (ET0) is one of the prominent hydrologic variables affecting water and energy balances and critical factors for crop water requirements and irrigation scheduling. Evapotranspiration is a complex hydrological variable defined by various climatic variables. Various empirical formulations have been developed to estimate ET0 depending upon the availability of meteorological variables. Such empirical formulations are region-specific and are for particular climatic conditions. In this context, mathematical models have emerged as simple and readily implementable for the estimation of ET0 with measured meteorological parameters as independent variables. Such data-driven models can be valuable to predict ET0 when climate data is insufficient. The present study compared various empirical models and data-driven algorithms to predict ET0 using various climate variables. Artificial neural networks (ANN) were adopted to estimate reference ET0. Four empirical methods P...