ARTIFICIAL INTELLIGENCE MODELS FOR ESTIMATING EVAPOTRANSPIRATION

Abstract

Evapotranspiration (ETo) is a complex, dynamic and non-linear hydrological process. Accurate estimation of ETo has long been an eminent topic of interest in the research community for its importance in effective planning and sustainable water resource management. Although the FAO-56 Penman-Monteith (PM) equation has been accepted as a standard equation for ETo measurement, the primary concern that inhibits the applicability of this equation is the requirement for all the climatological variables, which might not be available at a given location. Owning to the remarkable success and accuracy achieved by Artificial Intelligence (AI) in almost every sphere, scientists have proposed the usage AI models for ETo prediction as an alternate to the conventional methods. The artificial intelligence approach emerges as the best possible solution to map the relationships between climatic parameters and ET, even with limited knowledge of the interactions between variables. The results from the publications published over the last few years for ETo prediction using AI under varied agro-climatic scenarios have been analysed and synthesized. The advantages and disadvantages of the established AI techniques have been discussed in each subsection. The characteristics of the basic artificial intelligence models are also explored in this paper. Some of the derived insights and major findings are discussed. A research vision for the novice researchers in the applicability of the aforementioned techniques, in context of ETo prediction, but also be helpful as a compilation of the AI modelling studies for ETo prediction for the established water resource engineers and hydrologists.