Cited by 191Hydrological Forecasting Using AIHydrology and Watershed Management StudiesForecasting Techniques and Applications

Advancing monthly streamflow prediction accuracy of CART models using ensemble learning paradigms

Erdal, Halil Ibrahim, Karakurt, Onur·2013·Journal of Hydrology

Integrity signals

Registry

Crossref · 10.1016/j.jhydrol.2012.11.015

Cites

37 of 37 references verified0 citation errors · 0 not checkable

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  • The stochastic gradient boosting machine increases robustness by selecting samples randomly and has previously been employed in studies on river flow trend analysis and prediction.

    “This GBM increases robustness by selecting samples randomly, and has previously been employed in studies on Q trend analysis and prediction53,54.”

    From Burned forests impact water supplies (2018) · cites this paper as [54]

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Authors
Erdal, H. I., & Karakurt, O.
Affiliations
Gazi University (TR)
Venue
Journal of Hydrology
Publisher
Elsevier BV
Published
2013-01
Volume
477
Pages
119-128
ISSN
0022-1694 (ISSN-L)
OpenAlex
W2056950965
Cited by
191
References
37
Citations by year
2026: 7 · 2025: 12 · 2024: 20 · 2023: 18 · 2022: 20 · 2021: 25 · 2020: 23 · 2019: 15 · 2018: 13 · 2017: 7 · 2016: 12 · 2015: 9 · 2014: 6 · 2013: 4
FWCI
5.03
Type
journal article
Language
en
Open access
closed
Locations
Journal of Hydrology (publishedVersion)
Topics
  • Physical Sciences › Environmental Science › Environmental Engineering › Hydrological Forecasting Using AI
  • Physical Sciences › Environmental Science › Water Science and Technology › Hydrology and Watershed Management Studies
  • Social Sciences › Decision Sciences › Management Science and Operations Research › Forecasting Techniques and Applications
Keywords
monthly streamflow forecasting, classification and regression trees, ensemble learning, bagging, stochastic gradient boosting, support vector regression, hydrological forecasting, prediction accuracy

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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

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