Water, Год журнала: 2025, Номер 17(9), С. 1384 - 1384

Опубликована: Май 4, 2025

Evapotranspiration (ET) has a significant role in various natural and human systems, such as water cycle balance, climate regulation, ecosystem health, agriculture, hydrological cycle, resource management, studies. Among approaches that are employed for estimating ET, the Penman–Monteith equation is known widely accepted reference approach. However, extensive data requirement of this method crucial challenge limits its usage, particularly data-scarce regions. Therefore, an alternative approach, artificial intelligence (AI) models have gained prominence evapotranspiration because their capacity to handle complicated relationships between meteorological variables loss processes. These leverage large datasets advanced algorithms provide accurate timely ET predictions. The current research aims review previous studies addressing application AI model modeling under four main categories: neuron-based, tree-based, kernel-based, hybrid models. results study indicated traditional like (PM) require input data, while AI-based offer promising alternatives due ability complex nonlinear relationships. Despite potential, face challenges overfitting, interpretability, inconsistent variable selection, lack integration with physical processes, highlighting need standardized configurations, better pre-processing techniques, incorporation remote sensing data.

Язык: Английский