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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| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | , , |
| منشور في: |
2025
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| الموضوعات: | |
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| _version_ | 1852018261095350272 |
|---|---|
| author | Yeonuk Kim (4589488) |
| author2 | Monica Garcia (5507180) T. Andrew Black (1869484) Mark S. Johnson (166934) |
| author2_role | author author author |
| author_facet | Yeonuk Kim (4589488) Monica Garcia (5507180) T. Andrew Black (1869484) Mark S. Johnson (166934) |
| author_role | author |
| dc.creator.none.fl_str_mv | Yeonuk Kim (4589488) Monica Garcia (5507180) T. Andrew Black (1869484) Mark S. Johnson (166934) |
| dc.date.none.fl_str_mv | 2025-07-23T17:29:01Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pone.0328798.g002 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/figure/Flow_chart_of_the_six_hybrid_ET_models_and_a_pure_machine_learning_model_using_random_forest_RF_algorithms_/29628564 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | 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 |
| dc.title.none.fl_str_mv | Flow chart of the six hybrid ET models and a pure machine learning model using random forest (RF) algorithms. |
| dc.type.none.fl_str_mv | Image Figure info:eu-repo/semantics/publishedVersion image |
| description | <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>2</i></sub>), aerodynamic conductance (<i>g</i><sub><i>aH</i></sub>), global radiation (<i>Rg</i>), and available energy (<i>AE</i>).</p> |
| eu_rights_str_mv | openAccess |
| id | Manara_7ae9f6bc2207d86d9a5a519ff2fd3fce |
| identifier_str_mv | 10.1371/journal.pone.0328798.g002 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/29628564 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Flow chart of the six hybrid ET models and a pure machine learning model using random forest (RF) algorithms.Yeonuk Kim (4589488)Monica Garcia (5507180)T. Andrew Black (1869484)Mark S. Johnson (166934)BiotechnologyScience PolicyEnvironmental Sciences not elsewhere classifiedBiological Sciences not elsewhere classifiedtackle challenges inherentsitu meteorological datasatellite remote sensingrelatively stable duerandom forest algorithmnovel empirical parameterestimate terrestrial evapotranspirationdifferent physical formulationsconventional choices mayconstrained evapotranspiration modelsscale et observationsdiv >< paccurate hybrid modelpure ml modelrmse ), indicatingpure machine learninghybrid et modelshybrid modelset ),model errormodels employedmachine learninghybrid approachet estimateswell understoodstudy underscoresstrong correlationsquare errorresults implymechanisms drivinglearned parameterskey advantageimproves performanceimproved performanceextreme conditionsdomain knowledgecritical rolecarbon cyclesatmosphere equilibrium8 w<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>2</i></sub>), aerodynamic conductance (<i>g</i><sub><i>aH</i></sub>), global radiation (<i>Rg</i>), and available energy (<i>AE</i>).</p>2025-07-23T17:29:01ZImageFigureinfo:eu-repo/semantics/publishedVersionimage10.1371/journal.pone.0328798.g002https://figshare.com/articles/figure/Flow_chart_of_the_six_hybrid_ET_models_and_a_pure_machine_learning_model_using_random_forest_RF_algorithms_/29628564CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/296285642025-07-23T17:29:01Z |
| spellingShingle | Flow chart of the six hybrid ET models and a pure machine learning model using random forest (RF) algorithms. Yeonuk Kim (4589488) 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 |
| status_str | publishedVersion |
| title | Flow chart of the six hybrid ET models and a pure machine learning model using random forest (RF) algorithms. |
| title_full | Flow chart of the six hybrid ET models and a pure machine learning model using random forest (RF) algorithms. |
| title_fullStr | Flow chart of the six hybrid ET models and a pure machine learning model using random forest (RF) algorithms. |
| title_full_unstemmed | Flow chart of the six hybrid ET models and a pure machine learning model using random forest (RF) algorithms. |
| title_short | Flow chart of the six hybrid ET models and a pure machine learning model using random forest (RF) algorithms. |
| title_sort | Flow chart of the six hybrid ET models and a pure machine learning model using random forest (RF) algorithms. |
| topic | 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 |