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<p>Accurate estimation of reference evapotranspiration (ETo) is crucial for effective water resource management and irrigation planning, particularly in regions with significant agricultural activity, such as Doukkala, Morocco. This study explores the potential of machine learning (ML) models to estimate daily ETo using 18 years of climatic data collected from two meteorological stations in the region. The dataset includes key meteorological variables—temperature, humidity, solar radiation, wind speed, and rainfall—with ETo calculated using the FAO-56 Penman-Monteith (PM) equation as the reference. A range of ML models, including Artificial Neural Networks (ANN), Support Vector Regression (SVR), XGBoost, Random Forest (RF), k-Nearest Neighbors (KNN), Decision Trees (DT), and Multiple Linear Regression (MLR), were trained and compared. Additionally, deep learning architectures such as Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN) were evaluated for their ability to capture temporal dependencies within the data. Among the tested models, ANN demonstrated the highest performance, achieving an R² of 0.9868 and an MSE of 0.0440, followed closely by SVR and XGBoost, both of which also exhibited high predictive accuracy. In contrast, simpler models like MLR and DT showed comparatively lower performance, underscoring the advantages of more advanced ML techniques for ETo estimation. While LSTM displayed moderate success, CNN underperformed relative to other models. The inclusion of data from two stations enabled the evaluation of model generalizability across distinct locations within the region. However, this study has some limitations. While cross-site validation improves generalizability, the findings are based on two stations within the same semi-arid region, which may not fully represent ETo dynamics in more diverse climatic conditions. Additionally, while ML models demonstrated strong predictive capabilities, further improvements could be achieved by integrating hybrid modeling approaches to enhance long-term reliability in changing climatic conditions.</p>

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Evaluating machine learning models and feature selection for reference evapotranspiration estimation in semi-arid regions: a case study in Doukkala, Morocco

  • 期刊论文

  • 发表日期：

    2025年5月10日

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  • Zaid Belarbi,
  • Yacine EL Younoussi

摘要

Accurate estimation of reference evapotranspiration (ETo) is crucial for effective water resource management and irrigation planning, particularly in regions with significant agricultural activity, such as Doukkala, Morocco. This study explores the potential of machine learning (ML) models to estimate daily ETo using 18 years of climatic data collected from two meteorological stations in the region. The dataset includes key meteorological variables—temperature, humidity, solar radiation, wind speed, and rainfall—with ETo calculated using the FAO-56 Penman-Monteith (PM) equation as the reference. A range of ML models, including Artificial Neural Networks (ANN), Support Vector Regression (SVR), XGBoost, Random Forest (RF), k-Nearest Neighbors (KNN), Decision Trees (DT), and Multiple Linear Regression (MLR), were trained and compared. Additionally, deep learning architectures such as Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN) were evaluated for their ability to capture temporal dependencies within the data. Among the tested models, ANN demonstrated the highest performance, achieving an R² of 0.9868 and an MSE of 0.0440, followed closely by SVR and XGBoost, both of which also exhibited high predictive accuracy. In contrast, simpler models like MLR and DT showed comparatively lower performance, underscoring the advantages of more advanced ML techniques for ETo estimation. While LSTM displayed moderate success, CNN underperformed relative to other models. The inclusion of data from two stations enabled the evaluation of model generalizability across distinct locations within the region. However, this study has some limitations. While cross-site validation improves generalizability, the findings are based on two stations within the same semi-arid region, which may not fully represent ETo dynamics in more diverse climatic conditions. Additionally, while ML models demonstrated strong predictive capabilities, further improvements could be achieved by integrating hybrid modeling approaches to enhance long-term reliability in changing climatic conditions.