Exploring the Applicability of Regression Models and Artificial Neural Networks for Calculating Reference Evapotranspiration in Arid Regions

Abstract

Reference evapotranspiration (ET0) is critical in agriculture and irrigation water management, particularly in arid and semi-arid regions. Our study aimed to develop an accurate and efficient model for estimating ET0 using various climatic variables as predictors. This research evaluated two model techniques, i.e., stepwise regression and artificial neural networks (ANNs), to identify the most effective model for calculating ET0. The two models were developed and tested based on climate data obtained from the whole climatic station of Egypt. The CLIMWAT 2.0 program was used to acquire the climate data for Egypt from a total of 32 stations. This software is a dedicated meteorological database created specifically to work with the CROPWAT computer program. The models were developed using average climate data spanning 29 years, from 1991 to 2020. The obtained data were utilized to compute reference evapotranspiration using CROPWAT 8, based on the Penman–Monteith equation. The results showed that the ANN model demonstrated superior performance in ET0 calculations compared to other methods, achieving a coefficient of determination (R2) of 0.99 and a mean absolute percentage error (MAPE) of 2.7%. In contrast, the stepwise model regression yielded an R2 of 0.95 and an MAPE of 8.06. On the other hand, the most influential climatic variables were maximum temperature, humidity, solar radiation, and wind speed. The findings of this study could be applied in various fields, such as agriculture, irrigation, and crop water requirements, to optimize crop growth under limited water resources and global environmental changes. Furthermore, our study identifies the limitations and challenges of applying these models in arid regions, such as data availability constraints and model complexity. We discuss the need for more extensive and reliable datasets and suggest future research directions, including ensemble modeling, remote sensing data integration, and evaluating climate change’s impact on ET0 estimation. Overall, this study contributes to the understanding of ET0 estimation in arid regions and provides valuable insights into the applicability of regression models and ANNs. The superior performance of ANNs offers potential advancements in water resource management and agricultural planning, enabling more accurate and informed decision-making processes.