Artificial neural network model for prediction of crop evapotranspiration

Water is becoming a scarce and expensive resource in most civilized areas throughout the world. It is estimated that more than half of the world population depends on agricultural irrigated products. In agriculture, water requirements are associated with irrigation demand and its prediction involves a computation of many water balance parameters such as runoff, precipitation, water table contribution and evapotranspiration. Evapotranspiration is a major component of the hydrologic cycle. The estimation of evapotranspiration from vegetated surface is a basic tool to compute water balances and to estimate water availability and requirements. Accurate and consistent measurement of evapotranspiration for a longer duration has always posed a challenge for the researchers, while using both the direct and indirect method of measurement. The methods for measuring evapotranspiration require complex and costly instruments and are generally recommended only for specific research purposes. Evapotranspiration measurement by lysimeter is time consuming and needs precise and carefully planned experiments.