The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Monthly streamflow forecasting using Gaussian Process Regression
Open accessOverview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
The Nature of Statistical Learning Theory
Vapnik, V. N. (1995) · doi:10.1007/978-1-4757-2440-0 · Cited by 39,138
Gaussian Processes for Machine Learning
Rasmussen, C. E., & Williams, C. K. I. (2005) · doi:10.7551/mitpress/3206.001.0001 · Cited by 11,705
Ensemble Methods: Foundations and Algorithms
Zhou, Z.-H. (2012) · doi:10.1201/b12207 · Cited by 2,534
Beven, K., & Freer, J. (2001) · Journal of Hydrology · doi:10.1016/s0022-1694(01)00421-8 · Cited by 2,189
Cited by
Seasonal Drought Prediction: Advances, Challenges, and Future Prospects
Hao, Z., Singh, V. P., & Xia, Y. (2018) · Reviews of Geophysics · doi:10.1002/2016rg000549 · Cited by 607
Jiang, S., Zheng, Y., & Solomatine, D. (2020) · Geophysical Research Letters · doi:10.1029/2020gl088229 · Cited by 365
Guo, Y., Zhang, Y., Zhang, L., & Wang, Z. (2021) · WIREs Water · doi:10.1002/wat2.1487 · Cited by 336
Long lead-time daily and monthly streamflow forecasting using machine learning methods
Cheng, M., Fang, F., Kinouchi, T., Navon, I. M., & Pain, C. C. (2020) · Journal of Hydrology · doi:10.1016/j.jhydrol.2020.125376 · Cited by 312
Forecast Methods for Time Series Data: A Survey
Liu, Z., Zhu, Z., Gao, J., & Xu, C. (2021) · IEEE Access · doi:10.1109/access.2021.3091162 · Cited by 247
Metadata
- Authors
- Sun, A. Y., Wang, D., & Xu, X.
- Venue
- Journal of Hydrology
- Publisher
- Elsevier BV
- Published
- 2014-04
- Volume
- 511
- Pages
- 72-81
- ISSN
- 0022-1694 (ISSN-L)
- OpenAlex
- W2087523516
- Cited by
- 301
- References
- 4
- FWCI
- 6.81
- Type
- journal article
- Language
- en
- Open access
- green
About these checks
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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Monthly streamflow forecasting using Gaussian Process Regression. https://app.cochat.ai/papers/10.1016/j.jhydrol.2014.01.023
- The paper
- Sun, A. Y., Wang, D., & Xu, X. (2014). Monthly streamflow forecasting using Gaussian Process Regression. Journal of Hydrology. https://doi.org/10.1016/j.jhydrol.2014.01.023
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
