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Accurate prediction of reference evapotranspiration (ET0) is essential for effective management across various sectors, particularly in agriculture and irrigation. This paper presents a comparative analysis of three prominent machine-learning techniques, Support Vector Machines (SVM), Random Forest (RF), and K-Nearest Neighbors (KNN), utilizing a comprehensive dataset of meteorological variables. The study focuses on the eastern region of Morocco, specifically Oujda, where water resource management is of critical importance. Through rigorous experimentation and evaluation, our findings reveal SVM’s superior accuracy (R2 = 0.9981) in ET0 prediction when compared to RF and KNN. This superior performance underscores SVM's potential for precise ET0 forecasting, facilitating informed decision-making in water resource management.

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Machine Learning Forecasting Approaches for Evapotranspiration: A Comparative Analysis

  • 会议论文

  • 发表日期：

    2025年

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  • Hassan Mokhtari,
  • Mohammed Benzaouia,
  • Bekkay Hajji,
  • Nabil Ayadi,
  • Khalid Chaabane

摘要

Accurate prediction of reference evapotranspiration (ET0) is essential for effective management across various sectors, particularly in agriculture and irrigation. This paper presents a comparative analysis of three prominent machine-learning techniques, Support Vector Machines (SVM), Random Forest (RF), and K-Nearest Neighbors (KNN), utilizing a comprehensive dataset of meteorological variables. The study focuses on the eastern region of Morocco, specifically Oujda, where water resource management is of critical importance. Through rigorous experimentation and evaluation, our findings reveal SVM’s superior accuracy (R2 = 0.9981) in ET0 prediction when compared to RF and KNN. This superior performance underscores SVM's potential for precise ET0 forecasting, facilitating informed decision-making in water resource management.