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Author: search.filters.author.Faloye, Oluwaseun TemitopeSubject: search.filters.subject.NASA POWER

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    Performance evaluation of machine learning algorithms for estimating reference evapotranspiration based on NASA POWER weather data: a case study in Nigeria
    (2026-01-01)
    Faloye, Oluwaseun Temitope
    ;
    Awotoye, Grace
    ;
    Eludire, Oluwadamilare Oluwasegun
    ;
    Olaleye, Oluwatobi Solomon
    ;
    Oluwadare, Ayoola Olamitomi
    The Penman–Monteith (PM) method is recognized as the globally accepted approach for estimating reference evapotranspiration (ETo). However, its use is constrained in areas with limited or unavailable data. Predicting ETo using multiple support vector machine (SVM) kernels and decision tree (DT) ensembles with NASA POWER data is innovative, as previous SVM-based ETo prediction studies have relied primarily on linear kernels. This study aims to evaluate the performance of different machine learning (ML) models, specifically SVM and DT and their ensembles, using NASA Power data as input. For this purpose, ML models were trained using average values of the monthly climatic data (maximum and minimum air temperatures, relative humidity, and wind speed) from NASA POWER. ETo was used as the output variable and was calculated from ground-observed data using the PM method. The developed ML models underwent training and validation to determine ETo in areas with different weather conditions in Nigeria: Kano—dry weather, Onne—wet weather, and Ibadan—moderate weather. Thirty and 70 % of the data were used during training and validation, respectively. The SVMs used in this study include linear SVM, quadratic SVM, cubic SVM, fine Gaussian (FG) SVM, medium Gaussian (MG) SVM, and coarse Gaussian SVM. The decision trees include fine, medium, and coarse trees, along with their ensembles: bagged and boosted trees. The model performance was evaluated using various error metrics. The FG SVM model exhibited the most accurate and precise estimation of ETo, with root mean square error (RMSE) values of 0.38 and 0.599 mm during the training and testing phases, respectively. Additionally, the coefficient of determination (r<sup>2</sup>) was good, with values of 0.87 and 0.72 during training and validation. The FG SVM outperformed all other models across all study locations, demonstrating its robustness in predicting ETo despite the contrasting weather conditions. Overall, this study revealed that the integration of data from NASA POWER with FG SVM accurately estimated reference evapotranspiration, which is important for effective water resource management in areas where ground climatic data is unavailable.
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    Evaluation of various machine learning-based bias correction approaches for NASA POWER air temperatures: a case study of Nigeria
    (2025-01-01)
    Faloye, Oluwaseun Temitope
    ;
    Kamchoom, Viroon
    ;
    Ogunrinde, Akinwale T.
    ;
    Ajayi, Ayodele Ebenezer
    ;
    Oguntunde, Philip Gbenro
    Remotely sensed air temperature data from NASA POWER are widely used in regions with scarce climatic observations, particularly for agricultural applications such as calculating crop water requirements. This study employed a suite of machine learning (ML) algorithms to correct biases in NASA POWER air temperature outputs, including multiple support vector regression (SVR) variants—Linear SVR, Quadratic SVR, Cubic SVR, Fine Gaussian SVR, Medium Gaussian SVR, Coarse Gaussian SVR—and ensemble decision tree models: bagged trees (BGT) and boosted trees (BT). The objective of this study was to assess the ability of different ML algorithms to reduce biases in NASA POWER air temperature data, with the broader goal of identifying the most suitable ML method for air temperature bias correction in Nigeria. For this analysis, we used daily air temperature records from seven meteorological stations across diverse regions of Nigeria. The performance of NASA POWER minimum and maximum air temperature datasets was evaluated using standard error metrics. Subsequent application of ML algorithms significantly improved data accuracy: the normalized root mean square error (NRMSE) of the corrected outputs was mostly below 10%, indicating excellent predictive performance when ML was integrated. Among the SVR variants tested, Fine Gaussian SVR consistently yielded the best prediction results. This finding suggests that Fine Gaussian SVR is a robust tool for enhancing the reliability of air temperature data—critical for improving the accuracy of crop water requirement calculations in regions where in-situ air temperature observations are limited.