The DOI matches its Crossref record; 70 of the 70 references it cites were found in a registry or an index.
Applications of XGBoost in water resources engineering: A systematic literature review (Dec 2018–May 2023)
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Citations
Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
XGBoost: A Scalable Tree Boosting System
Chen, T., & Guestrin, C. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939785 · Cited by 54,569
Greedy function approximation: A gradient boosting machine
Friedman, J. H. (2001) · The Annals of Statistics · doi:10.1214/aos/1013203451 · Cited by 30,731
Quinlan, J. R. (1986) · Machine Learning · doi:10.1007/bf00116251 · Cited by 21,439
A decision-theoretic generalization of on-line learning and an application to boosting
Freund, Y., & Schapire, R. E. (1997) · Journal of Computer and System Sciences · doi:10.1006/jcss.1997.1504 · Cited by 20,466
Soil and Water Assessment Tool Theoretical Documentation Version 2009
Neitsch, S. L., Arnold, J. G., Kiniry, J. R., & Williams, J. T. (2011) · OakTrust (Texas A&M University Libraries) · Cited by 3,980
Shen, C. (2018) · Water Resources Research · doi:10.1029/2018wr022643 · Cited by 1,133
Tyralis, H., Papacharalampous, G., & Langousis, A. (2019) · Water · doi:10.3390/w11050910 · Cited by 771
Hybrid decision tree-based machine learning models for short-term water quality prediction
Lu, H., & Ma, X. (2020) · Chemosphere · doi:10.1016/j.chemosphere.2020.126169 · Cited by 642
Metadata
- Authors
- Niazkar, M., Menapace, A., Brentan, B., Piraei, R., Jimenez, D., Dhawan, P., & Righetti, M.
- Venue
- Environmental Modelling & Software
- Publisher
- Elsevier BV
- Published
- 2024-03
- Volume
- 174
- Pages
- 105971
- ISSN
- 1364-8152 (ISSN-L)
- OpenAlex
- W4391723149
- Cited by
- 404
- References
- 70
- FWCI
- 16.35
- Type
- journal article
- Language
- en
- Open access
- green
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- CoChat. (2026). Citation check: Applications of XGBoost in water resources engineering: A systematic literature review (Dec 2018–May 2023). https://app.cochat.ai/papers/10.1016/j.envsoft.2024.105971
- The paper
- Niazkar, M., Menapace, A., Brentan, B., Piraei, R., Jimenez, D., Dhawan, P., & Righetti, M. (2024). Applications of XGBoost in water resources engineering: A systematic literature review (Dec 2018–May 2023). Environmental Modelling & Software. https://doi.org/10.1016/j.envsoft.2024.105971
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.
