Performance evaluation of machine learning algorithms for estimating reference evapotranspiration based on NASA POWER weather data: a case study in Nigeria

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dc.contributor.authorOluwaseun Temitope Faloye
dc.contributor.authorGrace Awotoye
dc.contributor.authorOluwadamilare Oluwasegun Eludire
dc.contributor.authorOluwatobi Solomon Olaleye
dc.contributor.authorAyoola O. Oluwadare
dc.contributor.authorOluwafemi E. Adeyeri
dc.contributor.authorLaemthong Laokhongthavorn
dc.contributor.authorViroon Kamchoom
dc.date.accessioned2026-05-08T19:26:46Z
dc.date.issued2026-4-10
dc.description.abstract) 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.
dc.identifier.doi10.3389/frai.2026.1801981
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/20786
dc.publisherFrontiers in Artificial Intelligence
dc.subjectPlant Water Relations and Carbon Dynamics
dc.subjectHydrological Forecasting Using AI
dc.subjectAir Quality Monitoring and Forecasting
dc.titlePerformance evaluation of machine learning algorithms for estimating reference evapotranspiration based on NASA POWER weather data: a case study in Nigeria
dc.typeArticle

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