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Article

Gap-Filling of Surface Fluxes Using Machine Learning Algorithms in Various Ecosystems

Department of Bioenvironmental Systems Engineering, National Taiwan University, Taipei 10617, Taiwan
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Water 2020, 12(12), 3415; https://doi.org/10.3390/w12123415
Received: 10 September 2020 / Revised: 13 November 2020 / Accepted: 1 December 2020 / Published: 4 December 2020
(This article belongs to the Section Hydrology)
Five machine learning (ML) algorithms were employed for gap-filling surface fluxes of CO2, water vapor, and sensible heat above three different ecosystems: grassland, rice paddy field, and forest. The performance and limitations of these ML models, which are support vector machine, random forest, multi-layer perception, deep neural network, and long short-term memory, were investigated. Firstly, the accuracy of gap-filling to time and hysteresis input factors of ML algorithms for different ecosystems is discussed. Secondly, the optimal ML model selected in the first stage is compared with the classic method—the Penman–Monteith (P–M) equation for water vapor flux gap-filling. Thirdly, with different gap lengths (from one hour to one week), we explored the data length required for an ML model to perform the optimal gap-filling. Our results demonstrate the following: (1) for ecosystems with a strong hysteresis between surface fluxes and net radiation, adding proceeding meteorological data into the model inputs could improve the model performance; (2) the five ML models gave similar gap-filling performance; (3) for gap-filling water vapor flux, the ML model is better than the P–M equation; and (4) for a gap with length of half day, one day, or one week, an ML model with training data length greater than 1300 h would provide a better gap-filling accuracy. View Full-Text
Keywords: flux gap-filling; machine learning; Penman–Monteith equation; artificial neural network flux gap-filling; machine learning; Penman–Monteith equation; artificial neural network
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MDPI and ACS Style

Huang, I.-H.; Hsieh, C.-I. Gap-Filling of Surface Fluxes Using Machine Learning Algorithms in Various Ecosystems. Water 2020, 12, 3415. https://doi.org/10.3390/w12123415

AMA Style

Huang I-H, Hsieh C-I. Gap-Filling of Surface Fluxes Using Machine Learning Algorithms in Various Ecosystems. Water. 2020; 12(12):3415. https://doi.org/10.3390/w12123415

Chicago/Turabian Style

Huang, I-Hang, and Cheng-I Hsieh. 2020. "Gap-Filling of Surface Fluxes Using Machine Learning Algorithms in Various Ecosystems" Water 12, no. 12: 3415. https://doi.org/10.3390/w12123415

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