Comparing AquaCrop and machine learning regression models for predicting actual evapotranspiration in paddy crops: Surin province, Thailand

Optimizing agricultural irrigation water use is critical for crop efficiency and balancing supply and demand, under growing water stress driven by population growth. Crop growth models provide quantitative data for water management, but the development of a robust crop model is challenging due to unknown and uncertain parameters. This study evaluates and compares the predictive performance of six regression-based machine learning (ML) models against AquaCrop simulated actual evapotranspiration (AET) for paddy crops in Surin province, Thailand, using 40 years (1984–2023) of climate data. We compare three distinct scenarios: (1) all climate variables, (2) only highly correlated variables, and (3) only temperature data. Results show that the artificial neural network model effectively predicts AET, with a better performance across all scenarios. Reliable AET predictions through ML models are important for stakeholders in countries like Thailand, where the economy heavily depends on agriculture and paddy crop exports.