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Light Gradient Boosting Machine: An efficient soft computing model for estimating daily reference evapotranspiration with local and external meteorological data

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  1. Yamashiro, Hirochika & Nonaka, Hirofumi, 2021. "Estimation of processing time using machine learning and real factory data for optimization of parallel machine scheduling problem," Operations Research Perspectives, Elsevier, vol. 8(C).
  2. Zhu, Keyu & Zhao, Yuhong & Ma, Yongbo & Zhang, Qi & Kang, Zhen & Hu, Xiaohui, 2022. "Drip irrigation strategy for tomatoes grown in greenhouse on the basis of fuzzy Borda and K-means analysis method," Agricultural Water Management, Elsevier, vol. 267(C).
  3. Yi Zhang & Chunxiao Cheng & Zhihui Wang & Hongxin Hai & Lulu Miao, 2025. "Spatiotemporal Variation and Driving Factors of Carbon Sequestration Rate in Terrestrial Ecosystems of Ningxia, China," Land, MDPI, vol. 14(1), pages 1-18, January.
  4. Zheng, Jing & Fan, Junliang & Zhang, Fucang & Wu, Lifeng & Zou, Yufeng & Zhuang, Qianlai, 2021. "Estimation of rainfed maize transpiration under various mulching methods using modified Jarvis-Stewart model and hybrid support vector machine model with whale optimization algorithm," Agricultural Water Management, Elsevier, vol. 249(C).
  5. 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).
  6. 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).
  7. Mohamed Chaibi & EL Mahjoub Benghoulam & Lhoussaine Tarik & Mohamed Berrada & Abdellah El Hmaidi, 2021. "An Interpretable Machine Learning Model for Daily Global Solar Radiation Prediction," Energies, MDPI, vol. 14(21), pages 1-19, November.
  8. Ali Mokhtar & Nadhir Al-Ansari & Wessam El-Ssawy & Renata Graf & Pouya Aghelpour & Hongming He & Salma M. Hafez & Mohamed Abuarab, 2023. "Prediction of Irrigation Water Requirements for Green Beans-Based Machine Learning Algorithm Models in Arid Region," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(4), pages 1557-1580, March.
  9. Fei Zhang & Qing Yi & Rui Dong & Jin Yan & Xiao Xu, 2025. "Inner pace: A dynamic exploration and analysis of basketball game pace," PLOS ONE, Public Library of Science, vol. 20(5), pages 1-20, May.
  10. Tan, Daniel & Suvarna, Manu & Shee Tan, Yee & Li, Jie & Wang, Xiaonan, 2021. "A three-step machine learning framework for energy profiling, activity state prediction and production estimation in smart process manufacturing," Applied Energy, Elsevier, vol. 291(C).
  11. Ook Lee & Hanseon Joo & Hayoung Choi & Minjong Cheon, 2022. "Proposing an Integrated Approach to Analyzing ESG Data via Machine Learning and Deep Learning Algorithms," Sustainability, MDPI, vol. 14(14), pages 1-14, July.
  12. Ali Asghar Heidari & Mehdi Akhoondzadeh & Huiling Chen, 2022. "A Wavelet PM2.5 Prediction System Using Optimized Kernel Extreme Learning with Boruta-XGBoost Feature Selection," Mathematics, MDPI, vol. 10(19), pages 1-35, September.
  13. Rabeh Khalfaoui & Sami Ben Jabeur & Shawkat Hammoudeh & Wissal Ben Arfi, 2025. "The role of political risk, uncertainty, and crude oil in predicting stock markets: evidence from the UAE economy," Annals of Operations Research, Springer, vol. 345(2), pages 1105-1135, February.
  14. Zhe Dong & Yiyang Zhao & Anqi Wang & Meng Zhou, 2025. "Wind-Mambaformer: Ultra-Short-Term Wind Turbine Power Forecasting Based on Advanced Transformer and Mamba Models," Energies, MDPI, vol. 18(5), pages 1-22, February.
  15. Esangbedo, Moses Olabhele & Taiwo, Blessing Olamide & Abbas, Hawraa H. & Hosseini, Shahab & Sazid, Mohammed & Fissha, Yewuhalashet, 2024. "Enhancing the exploitation of natural resources for green energy: An application of LSTM-based meta-model for aluminum prices forecasting," Resources Policy, Elsevier, vol. 92(C).
  16. Houndekindo, Freddy & Ouarda, Taha B.M.J., 2024. "Prediction of hourly wind speed time series at unsampled locations using machine learning," Energy, Elsevier, vol. 299(C).
  17. 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.
  18. Vijendra Kumar & Naresh Kedam & Kul Vaibhav Sharma & Khaled Mohamed Khedher & Ayed Eid Alluqmani, 2023. "A Comparison of Machine Learning Models for Predicting Rainfall in Urban Metropolitan Cities," Sustainability, MDPI, vol. 15(18), pages 1-27, September.
  19. Yamaç, Sevim Seda, 2021. "Artificial intelligence methods reliably predict crop evapotranspiration with different combinations of meteorological data for sugar beet in a semiarid area," Agricultural Water Management, Elsevier, vol. 254(C).
  20. Wu, Lifeng & Peng, Youwen & Fan, Junliang & Wang, Yicheng & Huang, Guomin, 2021. "A novel kernel extreme learning machine model coupled with K-means clustering and firefly algorithm for estimating monthly reference evapotranspiration in parallel computation," Agricultural Water Management, Elsevier, vol. 245(C).
  21. Yan, Shicheng & Wu, Lifeng & Fan, Junliang & Zhang, Fucang & Zou, Yufeng & Wu, You, 2021. "A novel hybrid WOA-XGB model for estimating daily reference evapotranspiration using local and external meteorological data: Applications in arid and humid regions of China," Agricultural Water Management, Elsevier, vol. 244(C).
  22. Min Gan & Xijun Lai & Yan Guo & Yongping Chen & Shunqi Pan & Yinghao Zhang, 2024. "Floodplain Lake Water Level Prediction with Strong River-Lake Interaction Using the Ensemble Learning LightGBM," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(13), pages 5305-5321, October.
  23. Tianao Wu & Wei Zhang & Xiyun Jiao & Weihua Guo & Yousef Alhaj Hamoud, 2020. "Comparison of five Boosting-based models for estimating daily reference evapotranspiration with limited meteorological variables," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-28, June.
  24. Shima Amani & Hossein Shafizadeh-Moghadam & Saeid Morid, 2024. "Utilizing Machine Learning Models with Limited Meteorological Data as Alternatives for the FAO-56PM Model in Estimating Reference Evapotranspiration," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(6), pages 1921-1942, April.
  25. Huang, Guomin & Dong, Jianhua & Wu, Lifeng & Luo, Jingwei & Qiu, Rangjian & Cui, Yaokui & Wang, Yicheng, 2025. "A new regional reference evapotranspiration model based on quantile approximation of meteorological variables," Agricultural Water Management, Elsevier, vol. 308(C).
  26. Deliang Sun & Danlu Chen & Jialan Zhang & Changlin Mi & Qingyu Gu & Haijia Wen, 2023. "Landslide Susceptibility Mapping Based on Interpretable Machine Learning from the Perspective of Geomorphological Differentiation," Land, MDPI, vol. 12(5), pages 1-37, May.
  27. Ferreira, Lucas Borges & da Cunha, Fernando França, 2020. "New approach to estimate daily reference evapotranspiration based on hourly temperature and relative humidity using machine learning and deep learning," Agricultural Water Management, Elsevier, vol. 234(C).
  28. Fan, Junliang & Zheng, Jing & Wu, Lifeng & Zhang, Fucang, 2021. "Estimation of daily maize transpiration using support vector machines, extreme gradient boosting, artificial and deep neural networks models," Agricultural Water Management, Elsevier, vol. 245(C).
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