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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Vydané v:	Environmental earth sciences Ročník 80; číslo 22; s. 747
Hlavní autori:	Sahour, Hossein, Gholami, Vahid, Torkaman, Javad, Vazifedan, Mehdi, Saeedi, Sirwe
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Berlin/Heidelberg Springer Berlin Heidelberg 01.11.2021 Springer Nature B.V
Predmet:	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
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Popis
Shrnutí:	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 proposed to model the streamflow data in the three periods of growing seasons based on tree-rings and vessel features of the Zelkova carpinifolia species. First, full-disc samples were taken from 30 trees near the river, and the samples went through preprocessing, cross-dating, standardization, and time series analysis. Two machine learning algorithms, namely random forest (RF) and extreme gradient boosting (XGB), were used to model the relationships between dendrochronology variables (tree-rings and vessel features in the three periods of growing seasons) and the corresponding streamflow rates. The performance of each model was evaluated using statistical coefficients [coefficient of determination ( R -squared), Nash–Sutcliffe efficiency (NSE), and root-mean-square error (NRMSE)]. Findings demonstrate that consideration should be given to the XGB model in streamflow modeling given its apparent enhanced performance ( R -squared: 0.87; NSE: 0.81; and NRMSE: 0.43) over the RF model ( R -squared: 0.82; NSE: 0.71; and NRMSE: 0.52). Furthermore, the results showed that the models perform better in modeling the normal and low flows compared to extremely high flows. Finally, the tested models were used to reconstruct the temporal streamflow during the past decades (1970–1981).
Bibliografia:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ISSN:	1866-6280 1866-6299
DOI:	10.1007/s12665-021-10054-5