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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Bibliographic Details
Published in:Environmental earth sciences Vol. 80; no. 22; p. 747
Main Authors: Sahour, Hossein, Gholami, Vahid, Torkaman, Javad, Vazifedan, Mehdi, Saeedi, Sirwe
Format: Journal Article
Language:English
Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.11.2021
Springer Nature B.V
Subjects:
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 Access:Get full text
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Summary: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).
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ISSN:1866-6280
1866-6299
DOI:10.1007/s12665-021-10054-5