Estimating the Evapotranspiration using Hybrid Artificial Intelligence Techniques in Arid and Semi-Arid Regions of India

Author(s)	Mr. Pritam A. Mali, Dr. Amit P. Patil
Country	India
Abstract	Evapotranspiration (ET) is a crucial component of the hydrological cycle, particularly in arid and semi-arid regions where water resources are limited. Accurate estimation of ET is essential for effective water resource management and agricultural planning. This review explores the application of hybrid artificial intelligence (AI) techniques for estimating ET in the arid and semi-arid regions of India. By combining traditional AI methods, such as artificial neural networks (ANNs) and support vector machines (SVMs), with advanced optimization algorithms like genetic algorithms (GA), particle swarm optimization (PSO), and evolutionary strategies, researchers have enhanced the accuracy and reliability of ET predictions. This paper summarizes the state-of-the-art hybrid AI models, their methodologies, and the challenges and opportunities they present for managing water resources in India’s arid and semi arid zones.
Keywords	Evapotranspiration, Artificial neural networks, hybrid AI models
Field	Engineering
Published In	Volume 16, Issue 1, January-March 2025
Published On	2025-02-11
DOI	https://doi.org/10.71097/IJSAT.v16.i1.1749

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International Journal on Science and Technology