Makaleyi göster

Efficient estimation of Reference Evapotranspiration (ET₀) becomes necessary for water resources management and irrigation practices. Despite research advancement in the recent decades, results inconsistencies have been reported related to chaotic, stochastic and black box approaches for multi-step ahead prediction of ET₀. This study aimed at applying ensemble approaches to improve single and multi-step ahead predictions of ET₀. To do so, several Artificial Intelligence (AI) based techniques including Support Vector Regression (SVR), Adaptive Neuro Fuzzy Inference System (ANFIS), Artificial Neural Network (ANN) and Multiple Linear Regression (MLR) models, were employed for one, two and three-steps ahead predictions of ET₀ for numerous climatic stations in Iraq, North Cyprus and Turkey. Monthly meteorological parameters were used as inputs for the models development. Finally, two linear …