Utilization of Machine Learning Methods: Estimating Daily Evapotranspiration of Reference Plant Using Neural Networks in Razavi Khorasan Province

Document Type : Technical paper

Author

Zahra Shirmohammadi-Aliakbarkhani

Department of Water Science and Engineering, High Educational Complex of Torbat-e Jam, Torbat-e Jam, Iran

https://doi.org/10.22067/jwsd.v12i2.2501-1402

Abstract

Evapotranspiration is one of the most important components in water and energy balance, and its accurate estimation is necessary for water resource management, irrigation scheduling, and environmental studies. In recent years, the application of machine learning (ML) techniques has provided a promising approach to improving ET prediction accuracy. In this study, two artificial neural network models, multilayer perceptron (MLP) and radial basis function (RBF), were used to estimate reference crop ET at Mashhad, Sabzevar, and Torbat-e Jam stations in Razavi Khorasan province. Meteorological data, including temperature, relative humidity, wind speed, and sunshine hours, were collected and normalized from 1992 to 2023. The ET values calculated by the FAO Penman-Monteith method were used as target values for training the models. The results showed that the neural network models, especially MLP, could provide accurate estimates of ET and performed better than the Linacre and Hargreaves-Samani methods. Excluding wind speed at Torbat-e Jam and Mashhad stations resulted in a 5.1% decrease in the coefficient of determination (R²), while at Sabzevar, this decrease was 11.3%. In contrast, excluding sunshine hours had a negligible impact on model accuracy. These findings emphasize the high potential of artificial neural networks in estimating ET in data-scarce conditions and can contribute to the optimal management of agricultural water resources in the studied region.

Keywords

Evapotranspiration

Machine Learning

Neural Networks

Modeling

Water Management

Subjects

• Irrigation and Agriculture

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References

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