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.
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