A Brief Review of Random Forests for Water Scientists and Practitioners and Their Recent History in Water Resources
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, whi...
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| Vydáno v: | Water (Basel) Ročník 11; číslo 5; s. 910 |
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| Hlavní autoři: | , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Basel
MDPI AG
01.05.2019
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| Témata: | |
| ISSN: | 2073-4441, 2073-4441 |
| On-line přístup: | Získat plný text |
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| Abstract | 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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| AbstractList | 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. |
| Author | Langousis, Andreas Tyralis, Hristos Papacharalampous, Georgia |
| Author_xml | – sequence: 1 givenname: Hristos orcidid: 0000-0002-8932-4997 surname: Tyralis fullname: Tyralis, Hristos – sequence: 2 givenname: Georgia orcidid: 0000-0001-5446-954X surname: Papacharalampous fullname: Papacharalampous, Georgia – sequence: 3 givenname: Andreas orcidid: 0000-0002-0643-2520 surname: Langousis fullname: Langousis, Andreas |
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| SubjectTerms | Algorithms Artificial intelligence Big Data Classification computer software Decision trees Feature selection Hydrology Machine learning Science Scientists Software Survival analysis Variables water resources water utilities |
| Title | A Brief Review of Random Forests for Water Scientists and Practitioners and Their Recent History in Water Resources |
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