IDEAS home Printed from https://ideas.repec.org/r/eee/agiwat/v217y2019icp303-315.html

Evapotranspiration evaluation models based on machine learning algorithms—A comparative study

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
as


Cited by:

  1. So Young Woo & Chung Gil Jung & Ji Wan Lee & Seong Joon Kim, 2019. "Evaluation of Watershed Scale Aquatic Ecosystem Health by SWAT Modeling and Random Forest Technique," Sustainability, MDPI, vol. 11(12), pages 1-15, June.
  2. Mohammadi, Babak & Mehdizadeh, Saeid, 2020. "Modeling daily reference evapotranspiration via a novel approach based on support vector regression coupled with whale optimization algorithm," Agricultural Water Management, Elsevier, vol. 237(C).
  3. Yin, Juan & Deng, Zhen & Ines, Amor V.M. & Wu, Junbin & Rasu, Eeswaran, 2020. "Forecast of short-term daily reference evapotranspiration under limited meteorological variables using a hybrid bi-directional long short-term memory model (Bi-LSTM)," Agricultural Water Management, Elsevier, vol. 242(C).
  4. Chia, Min Yan & Huang, Yuk Feng & Koo, Chai Hoon, 2022. "Resolving data-hungry nature of machine learning reference evapotranspiration estimating models using inter-model ensembles with various data management schemes," Agricultural Water Management, Elsevier, vol. 261(C).
  5. Zhu, Wenbin & Yu, Xiaoyu & Wei, Jiaxing & Lv, Aifeng, 2024. "Surface flux equilibrium estimates of evaporative fraction and evapotranspiration at global scale: Accuracy evaluation and performance comparison," Agricultural Water Management, Elsevier, vol. 291(C).
  6. Fang, Heng & Li, Yuannong & Gu, Xiaobo & Du, Yadan & Chen, Pengpeng & Hu, Hongxiang, 2024. "Evapotranspiration, water use efficiency, and yield for film mulched maize under different nitrogen-fertilization rates and climate conditions," Agricultural Water Management, Elsevier, vol. 301(C).
  7. Fuentes, Sigfredo & Ortega-Farías, Samuel & Carrasco-Benavides, Marcos & Tongson, Eden & Gonzalez Viejo, Claudia, 2024. "Actual evapotranspiration and energy balance estimation from vineyards using micro-meteorological data and machine learning modeling," Agricultural Water Management, Elsevier, vol. 297(C).
  8. Chia, Min Yan & Huang, Yuk Feng & Koo, Chai Hoon, 2021. "Swarm-based optimization as stochastic training strategy for estimation of reference evapotranspiration using extreme learning machine," Agricultural Water Management, Elsevier, vol. 243(C).
  9. De Caro, Dario & Ippolito, Matteo & Cannarozzo, Marcella & Provenzano, Giuseppe & Ciraolo, Giuseppe, 2023. "Assessing the performance of the Gaussian Process Regression algorithm to fill gaps in the time-series of daily actual evapotranspiration of different crops in temperate and continental zones using ground and remotely sensed data," Agricultural Water Management, Elsevier, vol. 290(C).
  10. Ohana-Levi, Noa & Ben-Gal, Alon & Munitz, Sarel & Netzer, Yishai, 2022. "Grapevine crop evapotranspiration and crop coefficient forecasting using linear and non-linear multiple regression models," Agricultural Water Management, Elsevier, vol. 262(C).
  11. Tingting Deng & Hang Lyu & Rong Zhong & Yunting Zou & Jianan Wang & Yu Li & Yue Guo, 2026. "Incorporating Ecoregions and Freeze–Thaw Effects Effectively Improves the Accuracy of Machine Learning Models for Baseflow Prediction in Seasonally Frozen Regions," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 40(3), pages 1-16, February.
  12. Soo-Jin Kim & Seung-Jong Bae & Min-Won Jang, 2022. "Linear Regression Machine Learning Algorithms for Estimating Reference Evapotranspiration Using Limited Climate Data," Sustainability, MDPI, vol. 14(18), pages 1-20, September.
  13. Roy, Dilip Kumar & Lal, Alvin & Sarker, Khokan Kumer & Saha, Kowshik Kumar & Datta, Bithin, 2021. "Optimization algorithms as training approaches for prediction of reference evapotranspiration using adaptive neuro fuzzy inference system," Agricultural Water Management, Elsevier, vol. 255(C).
  14. Malik, Anurag & Jamei, Mehdi & Ali, Mumtaz & Prasad, Ramendra & Karbasi, Masoud & Yaseen, Zaher Mundher, 2022. "Multi-step daily forecasting of reference evapotranspiration for different climates of India: A modern multivariate complementary technique reinforced with ridge regression feature selection," Agricultural Water Management, Elsevier, vol. 272(C).
  15. Granata, Francesco & Di Nunno, Fabio, 2021. "Forecasting evapotranspiration in different climates using ensembles of recurrent neural networks," Agricultural Water Management, Elsevier, vol. 255(C).
  16. Ali Barzkar & Mohammad Najafzadeh & Farshad Homaei, 2022. "Evaluation of drought events in various climatic conditions using data-driven models and a reliability-based probabilistic model," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 110(3), pages 1931-1952, February.
  17. Erdem Küçüktopcu & Emirhan Cemek & Bilal Cemek & Halis Simsek, 2023. "Hybrid Statistical and Machine Learning Methods for Daily Evapotranspiration Modeling," Sustainability, MDPI, vol. 15(7), pages 1-15, March.
  18. Halil Karahan & Mahmut Cetin & Muge Erkan Can & Omar Alsenjar, 2024. "Developing a New ANN Model to Estimate Daily Actual Evapotranspiration Using Limited Climatic Data and Remote Sensing Techniques for Sustainable Water Management," Sustainability, MDPI, vol. 16(6), pages 1-17, March.
  19. Kelechi Igwe & Vaishali Sharda & Trevor Hefley, 2023. "Evaluating the Impact of Future Seasonal Climate Extremes on Crop Evapotranspiration of Maize in Western Kansas Using a Machine Learning Approach," Land, MDPI, vol. 12(8), pages 1-26, July.
  20. Jamei, Mehdi & Maroufpoor, Saman & Aminpour, Younes & Karbasi, Masoud & Malik, Anurag & Karimi, Bakhtiar, 2022. "Developing hybrid data-intelligent method using Boruta-random forest optimizer for simulation of nitrate distribution pattern," Agricultural Water Management, Elsevier, vol. 270(C).
  21. Fabio Di Nunno & Marco De Matteo & Giovanni Izzo & Francesco Granata, 2023. "A Combined Clustering and Trends Analysis Approach for Characterizing Reference Evapotranspiration in Veneto," Sustainability, MDPI, vol. 15(14), pages 1-23, July.
  22. Chaoqing Huang & Chao He & Qian Wu & MinhThu Nguyen & Song Hong, 2023. "Classification of the Land Cover of a Megacity in ASEAN Using Two Band Combinations and Three Machine Learning Algorithms: A Case Study in Ho Chi Minh City," Sustainability, MDPI, vol. 15(8), pages 1-27, April.
  23. Gaur, Srishti & Aslan-Sungur (Rojda), Guler & VanLoocke, Andy & Drewry, Darren T., 2025. "Explainable machine learning to quantify the value of proximal remote sensing in latent energy flux estimation," Agricultural Water Management, Elsevier, vol. 317(C).
  24. Beáta Novotná & Ľuboš Jurík & Ján Čimo & Jozef Palkovič & Branislav Chvíla & Vladimír Kišš, 2022. "Machine Learning for Pan Evaporation Modeling in Different Agroclimatic Zones of the Slovak Republic (Macro-Regions)," Sustainability, MDPI, vol. 14(6), pages 1-22, March.
  25. Jiang, Xiaoman & Wang, Yuntao & A., Yinglan & Wang, Guoqiang & Zhang, Xiaojing & Ma, Guangwen & Duan, Limin & Liu, Kai, 2024. "Optimizing actual evapotranspiration simulation to identify evapotranspiration partitioning variations: A fusion of physical processes and machine learning techniques," Agricultural Water Management, Elsevier, vol. 295(C).
  26. Zhang, Lei & Zhao, Xin & Zhu, Ge & He, Jun & Chen, Jian & Chen, Zhicheng & Traore, Seydou & Liu, Junguo & Singh, Vijay P., 2023. "Short-term daily reference evapotranspiration forecasting using temperature-based deep learning models in different climate zones in China," Agricultural Water Management, Elsevier, vol. 289(C).
  27. Mohammad Taghi Sattari & Halit Apaydin & Shahaboddin Shamshirband, 2020. "Performance Evaluation of Deep Learning-Based Gated Recurrent Units (GRUs) and Tree-Based Models for Estimating ETo by Using Limited Meteorological Variables," Mathematics, MDPI, vol. 8(6), pages 1-18, June.
  28. Phon Sheng Hou & Lokman Mohd Fadzil & Selvakumar Manickam & Mahmood A. Al-Shareeda, 2023. "Vector Autoregression Model-Based Forecasting of Reference Evapotranspiration in Malaysia," Sustainability, MDPI, vol. 15(4), pages 1-18, February.
  29. Pouya Aghelpour & Hadigheh Bahrami-Pichaghchi & Farzaneh Karimpour, 2022. "Estimating Daily Rice Crop Evapotranspiration in Limited Climatic Data and Utilizing the Soft Computing Algorithms MLP, RBF, GRNN, and GMDH," Complexity, John Wiley & Sons, vol. 2022(1).
  30. Elbeltagi, Ahmed & Deng, Jinsong & Wang, Ke & Hong, Yang, 2020. "Crop Water footprint estimation and modeling using an artificial neural network approach in the Nile Delta, Egypt," Agricultural Water Management, Elsevier, vol. 235(C).
  31. Fu, Chong & Song, Xiaoyu & Li, Lanjun & Zhao, Xinkai & Meng, Pengfei & Wang, Long & Wei, Wanyin & Guo, Songle & Zhu, Deming & He, Xi & Yang, Dongdan & Li, Huaiyou, 2024. "Combining the FAO-56 method and the complementary principle to partition the evapotranspiration of typical plantations and grasslands in the Chinese Loess Plateau," Agricultural Water Management, Elsevier, vol. 295(C).
  32. Filgueiras, Roberto & Almeida, Thomé Simpliciano & Mantovani, Everardo Chartuni & Dias, Santos Henrique Brant & Fernandes-Filho, Elpídio Inácio & da Cunha, Fernando França & Venancio, Luan Peroni, 2020. "Soil water content and actual evapotranspiration predictions using regression algorithms and remote sensing data," Agricultural Water Management, Elsevier, vol. 241(C).
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.