Flow chart of the six hybrid ET models and a pure machine learning model using random forest (RF) algorithms.

Flow chart of the six hybrid ET models and a pure machine learning model using random forest (RF) algorithms.

<p>The model input variables include soil moisture (SM), vegetation water content (VegWC), fraction of photosynthetically active radiation (FPAR), air temperature (T), relative humidity (<i>RH</i>), CO<sub>2</sub> concentration (<i>CO</i><sub><i>...

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Bibliographic Details
Main Author:	Yeonuk Kim (4589488) (author)
Other Authors:	Monica Garcia (5507180) (author), T. Andrew Black (1869484) (author), Mark S. Johnson (166934) (author)
Published:	2025
Subjects:	Biotechnology Science Policy Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified tackle challenges inherent situ meteorological data satellite remote sensing relatively stable due random forest algorithm novel empirical parameter estimate terrestrial evapotranspiration different physical formulations conventional choices may constrained evapotranspiration models scale et observations div >< p accurate hybrid model pure ml model rmse ), indicating pure machine learning hybrid et models hybrid models et ), model error models employed machine learning hybrid approach et estimates well understood study underscores strong correlation square error results imply mechanisms driving learned parameters key advantage improves performance improved performance extreme conditions domain knowledge critical role carbon cycles atmosphere equilibrium 8 w
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