Random forest and extreme gradient boosting algorithms for streamflow modeling using vessel features and tree-rings

Monitoring temporal variation of streamflow is necessary for many water resources management plans, yet, such practices are constrained by the absence or paucity of data in many rivers around the world. Using a permanent river in the north of Iran as a test site, a machine learning framework was pro...

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Veröffentlicht in:	Environmental earth sciences Jg. 80; H. 22; S. 747
Hauptverfasser:	Sahour, Hossein, Gholami, Vahid, Torkaman, Javad, Vazifedan, Mehdi, Saeedi, Sirwe
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Berlin/Heidelberg Springer Berlin Heidelberg 01.11.2021 Springer Nature B.V
Schlagworte:	Algorithms Biogeosciences Coefficients Dendrochronology Earth and Environmental Science Earth Sciences Environmental Science and Engineering Geochemistry Geology Growing season High flow Hydrologic data Hydrology/Water Resources Iran Learning algorithms Low flow Machine learning Mathematical models Modelling Original Article Performance enhancement Rivers Standardization Statistical methods Stream discharge Stream flow Streamflow data temporal variation Temporal variations Terrestrial Pollution Time series time series analysis Tree rings Trees Vessels Water resources Water resources management Iran XGB RF Dendrochronology Streamflow Machine learning Growing season
ISSN:	1866-6280, 1866-6299
Online-Zugang:	Volltext
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