Implementation of evolutionary computing models for reference evapotranspiration modeling: short review, assessment and possible future research directions

Evapotranspiration is one of the most important components of the hydrological cycle as it accounts for more than two-thirds of the global precipitation losses. Indeed, the accurate prediction of reference evapotranspiration (ET_o) is highly significant for many watershed activities, including agriculture, water management, crop production and several other applications. Therefore, reliable estimation of ET_o is a major concern in hydrology. ET_o can be estimated using different approaches, including field measurement, empirical formulation and mathematical equations. Most recently, advanced machine learning models have been developed for the estimation of ET_o. Among several machine learning models, evolutionary computing (EC) has demonstrated a remarkable progression in the modeling of ET_o. The current research is devoted to providing a new milestone in the implementation of the EC algorithm for the modeling of ET_o. A comprehensive review is conducted to recognize the feasibility of EC models and their potential in simulating ET_o in a wide range of environments. Evaluation and assessment of the models are also presented based on the review. Finally, several possible future research directions are proposed for the investigations of ET_o using EC.