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Volume 13
Issue 9
10.3390/atmos13091467
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Article

Estimation of Potential Evapotranspiration in the Yellow River Basin Using Machine Learning Models

by
Jie Liu
1,2,
Kunxia Yu
1,2,*,
Peng Li
1,2,
Lu Jia
1,2,
Xiaoming Zhang
3,
Zhi Yang
4 and
Yang Zhao
3
1
State Key Laboratory of Eco-Hydraulics in Northwest Arid Region of China, Xi’an University of Technology, Xi’an 710048, China
2
Key Laboratory of National Forestry Administration on Ecological Hydrology and Disaster Prevention in Arid Regions, Xi’an University of Technology, Xi’an 710048, China
3
State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, Institute of Water Resources and Hydropower Research, Beijing 100048, China
4
Ningxia Soil and Water Conservation Monitoring Station, Yinchuan 750002, China
*
Author to whom correspondence should be addressed.
Atmosphere 2022, 13(9), 1467; https://doi.org/10.3390/atmos13091467
Submission received: 14 August 2022 / Revised: 26 August 2022 / Accepted: 7 September 2022 / Published: 9 September 2022
(This article belongs to the Section Atmospheric Techniques, Instruments, and Modeling)
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Abstract

Potential evapotranspiration (PET) is an important input variable of many ecohydrological models, but commonly used empirical models usually input numerous meteorological factors. In consideration of machine learning for complex nonlinear learning, we evaluated the applicability of three machine learning algorithms in PET estimation in the Yellow River basin (YRB), in addition to determining significant factors affecting the accuracy of machine learning. Furthermore, the importance of meteorological factors at varying altitudes and drought index grades for PET simulation were evaluated. The results show that the accuracy of PET simulation in the YRB depends on the input of various meteorological factors; however, machine learning models including average temperature (Tmean) and sunshine hours (n) as input achieved satisfactory accuracy in the absence of complete meteorological data. Random forest generally performed best among all investigated models, followed by extreme learning machine, whereas empirical models overestimated or underestimated PET. The importance index shows that Tmean is the most influential factor with respect to PET, followed by n, and the influence of Tmean on PET gradually decreased with increased altitude and drier climate, whereas the influence of n shows the opposite trend.
Keywords: potential evapotranspiration; machine learning; empirical model; importance index; Yellow River basin potential evapotranspiration; machine learning; empirical model; importance index; Yellow River basin

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MDPI and ACS Style

Liu, J.; Yu, K.; Li, P.; Jia, L.; Zhang, X.; Yang, Z.; Zhao, Y. Estimation of Potential Evapotranspiration in the Yellow River Basin Using Machine Learning Models. Atmosphere 2022, 13, 1467. https://doi.org/10.3390/atmos13091467

AMA Style

Liu J, Yu K, Li P, Jia L, Zhang X, Yang Z, Zhao Y. Estimation of Potential Evapotranspiration in the Yellow River Basin Using Machine Learning Models. Atmosphere. 2022; 13(9):1467. https://doi.org/10.3390/atmos13091467

Chicago/Turabian Style

Liu, Jie, Kunxia Yu, Peng Li, Lu Jia, Xiaoming Zhang, Zhi Yang, and Yang Zhao. 2022. "Estimation of Potential Evapotranspiration in the Yellow River Basin Using Machine Learning Models" Atmosphere 13, no. 9: 1467. https://doi.org/10.3390/atmos13091467

APA Style

Liu, J., Yu, K., Li, P., Jia, L., Zhang, X., Yang, Z., & Zhao, Y. (2022). Estimation of Potential Evapotranspiration in the Yellow River Basin Using Machine Learning Models. Atmosphere, 13(9), 1467. https://doi.org/10.3390/atmos13091467

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