hybrid model to improve reference evapotranspiration prediction: Integrating ANN and PSO

Abstract

Reference evapotranspiration (ETo), one of the key elements of the hydrological cycle, is crucial for managing irrigation and drainage systems. In order to estimate monthly ETo, this study tested the prediction abilities of a unique hybrid methodology that coupled data pre-processing with a hybrid model composed of an artificial neural network (ANN) and particle swarm optimisation (PSO). In order to train and evaluate the model, monthly meteorological data were collected in Al-Kut City, Iraq, from 1990 to 2020. A range of statistical indicators were used to assess the model, including RMSE, NSE, and R2. The outcomes showed that the model, with a coefficient of determination of 0.93, is effective and has good simulation levels.