The DOI matches its Crossref record; 267 of the 267 references it cites were found in a registry or an index.
A Brief Review of Random Forests for Water Scientists and Practitioners and Their Recent History in Water Resources
Open accessAbstract
Random forests (RF) is a supervised machine learning algorithm, which has recently started to gain prominence in water resources applications. However, existing applications are generally restricted to the implementation of Breiman’s original algorithm for regression and classification problems, while numerous developments could be also useful in solving diverse practical problems in the water sector. Here we popularize RF and their variants for the practicing water scientist, and discuss related concepts and techniques, which have received less attention from the water science and hydrologic communities. In doing so, we review RF applications in water resources, highlight the potential of the original algorithm and its variants, and assess the degree of RF exploitation in a diverse range of applications. Relevant implementations of random forests, as well as related concepts and techniques in the R programming language, are also covered.
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Cites
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Artificial intelligence for sustainability: Challenges, opportunities, and a research agenda
Nishant, R., Kennedy, M., & Corbett, J. (2020) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2020.102104 · Cited by 1,203
Niazkar, M., Menapace, A., Brentan, B., Piraei, R., Jimenez, D., Dhawan, P., & Righetti, M. (2024) · Environmental Modelling & Software · doi:10.1016/j.envsoft.2024.105971 · Cited by 404
Niu, G., Yang, P., Zheng, Y., Cai, X., & Qin, H. (2021) · Water Resources Research · doi:10.1029/2020wr029121 · Cited by 19
Rao, W., Qian, X., Fan, Y., & Liu, T. (2023) · Science of The Total Environment · doi:10.1016/j.scitotenv.2023.161543 · Cited by 15
Parsimonious statistical learning models for low flow estimation
Laimighofer, J., Melcher, M., & Laaha, G. (2021) · doi:10.5194/hess-2021-481 · Cited by 30
“Finally, we use two models that are popular in hydrology: (vi) RF, (Tyralis et al., 2019) and (vii) SVR (Sujay Raghavendra and Deka, 2014).”
MethodInfluential
Metadata
- Authors
- Tyralis, H., Papacharalampous, G., & Langousis, A.
- Venue
- Water
- Publisher
- MDPI AG
- Published
- 2019-04-30
- Volume
- 11
- Issue
- 5
- Pages
- 910
- ISSN
- 2073-4441 (ISSN-L)
- OpenAlex
- W2943160824
- Cited by
- 771
- References
- 267
- FWCI
- 26.95
- Type
- journal article
- Language
- en
- Open access
- gold
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