Evapotranspiration (ET o ) estimation plays a crucial role in management of water resources, agricultural planning and environmental monitoring besides providing essential insights into water usage and irrigation scheduling. The FAO-56 Penman–Monteith (PM) method offers accurate ET o estimates based on meteorological data. However, this method often requires precise and comprehensive data, which may not always be readily available. Moreover, in recent years, artificial intelligence (AI) techniques have gained prominence as innovative solutions for ET o estimation, potentially addressing some of the limitations associated with traditional methods. The scope of this review is to evaluate and synthesize recent advancements in AI-based approaches for ET₀ estimation, emphasizing model performance, key input variables, advantages, limitations, and their comparison with traditional methods. To achieve this, a systematic review was conducted in accordance with the PRISMA 2020 guidelines. This review explores the application of various AI techniques viz . machine learning algorithms (e.g., Support Vector Machines, Neural Networks, and Random Forests), ensemble models, and optimization methods for ET o estimation during the period 2012–2024. By evaluating these AI-driven approaches and comparing them with the standard FAO-56 PM method, the review aimed to assess their effectiveness in improving estimation accuracy, computational efficiency and adaptability to different conditions. This study systematically reviewed applicability of AI techniques to ensure accuracy of ET o estimation, focusing on their ability to leverage diverse data sources, handle missing or incomplete information and adapt to varying climatic conditions. Moreover, the potential of hybrid models that combine AI with traditional methods to enhance predictive capabilities is also being discussed. By synthesizing current research and identifying gaps in the literature, this study provides insights into future directions for ET o estimation using AI techniques leading to judicious irrigation scheduling for improving water productivity and ensuring sustainable agricultural practices.
Gupta et al. (Mon,) studied this question.