Linear Regression Machine Learning Algorithms for Estimating Reference Evapotranspiration Using Limited Climate Data
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- Granata, Francesco, 2019. "Evapotranspiration evaluation models based on machine learning algorithms—A comparative study," Agricultural Water Management, Elsevier, vol. 217(C), pages 303-315.
- Ferreira, Lucas Borges & da Cunha, Fernando França & Fernandes Filho, Elpídio Inácio, 2022. "Exploring machine learning and multi-task learning to estimate meteorological data and reference evapotranspiration across Brazil," Agricultural Water Management, Elsevier, vol. 259(C).
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- Berti, Antonio & Tardivo, Gianmarco & Chiaudani, Alessandro & Rech, Francesco & Borin, Maurizio, 2014. "Assessing reference evapotranspiration by the Hargreaves method in north-eastern Italy," Agricultural Water Management, Elsevier, vol. 140(C), pages 20-25.
- Ahmadi, Farshad & Mehdizadeh, Saeid & Mohammadi, Babak & Pham, Quoc Bao & DOAN, Thi Ngoc Canh & Vo, Ngoc Duong, 2021. "Application of an artificial intelligence technique enhanced with intelligent water drops for monthly reference evapotranspiration estimation," Agricultural Water Management, Elsevier, vol. 244(C).
- Abderraouf Elferchichi & Giuseppina A. Giorgio & Nicola Lamaddalena & Maria Ragosta & Vito Telesca, 2017. "Variability of Temperature and Its Impact on Reference Evapotranspiration: The Test Case of the Apulia Region (Southern Italy)," Sustainability, MDPI, vol. 9(12), pages 1-15, December.
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- Jayashree T R & NV Subba Reddy & U Dinesh Acharya, 2023. "Modeling Daily Reference Evapotranspiration from Climate Variables: Assessment of Bagging and Boosting Regression Approaches," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(3), pages 1013-1032, February.
- Inga Dailidienė & Inesa Servaitė & Remigijus Dailidė & Erika Vasiliauskienė & Lolita Rapolienė & Ramūnas Povilanskas & Donatas Valiukas, 2023. "Increasing Trends of Heat Waves and Tropical Nights in Coastal Regions (The Case Study of Lithuania Seaside Cities)," Sustainability, MDPI, vol. 15(19), pages 1-21, September.
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Keywords
linear regression; machine learning; Penman–Monteith; polynomial regression; reference evapotranspiration;All these keywords.
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