Selecting essential factors for predicting reference crop evapotranspiration through tree-based machine learning and Bayesian optimization-Reference-Cited by-同舟云学术

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Selecting essential factors for predicting reference crop evapotranspiration through tree-based machine learning and Bayesian optimization

Published:2023-12-20 Issue: Volume: Page:
ISSN:0177-798X
Container-title:Theoretical and Applied Climatology
language:en
Short-container-title:Theor Appl Climatol

Author:

Zhao Long,Wang Yuhang,Shi Yi,Zhao Xinbo,Cui Ningbo,Zhang Shuo

Funder

National Natural Science Foundation of China

Fundamental Research Funds for the Central Universities

Key R&D and Promotion Projects in Henan Province

Experimental Technology Development Fund Project of Henan University of Science and Technology

PhD Research Startup Foundation of Henan University of Science and Technology

Henan Provincial Tobacco Company Luoyang City Company Technology Innovation Pro

Key Scientific Research Projects of Colleges and Universities in Henan Province

Publisher

Springer Science and Business Media LLC

Subject

Atmospheric Science

Link

https://link.springer.com/content/pdf/10.1007/s00704-023-04760-2.pdf

Reference32 articles.

1. Abdullah SS, Malek MA, Abdullah NS et al (2015) Extreme learning machines: a new approach for prediction of reference evapotranspiration. J Hydrol 527:184–195. https://doi.org/10.1016/j.jhydrol.2015.04.073

2. Ahmad MJ, Choi KS (2018) Influence of climate variables on FAO Penman-Monteith reference evapotranspiration in the Upper Chenab Canal command area of Pakistan. Paddy Water Environ, 16(3):425–438. https://doi.org/10.1007/s10333-018-0636-0

3. Alibabaei K, Pedro DG, Lima TM (2021) Modeling soil water content and reference evapotranspiration from climate data using deep learning method. Appl Sci 11(11):5029. https://doi.org/10.3390/app11115029

4. Alipour A, Yarahmadi J, Mahdavi M (2014) Comparative study of M5 model tree and artificial neural network in estimating reference evapotranspiration using MODIS products. J Climatol 2014:1–11. https://doi.org/10.1155/2014/839205

5. Alizamir M, Kisi O, Adnan RM et al (2020) Modelling reference evapotranspiration by combining neuro-fuzzy and evolutionary strategies. Acta Geophys 68(3):14. https://doi.org/10.1007/s11600-020-00446-9

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