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 pro...
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2019-04-01
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| Online Access: | https://www.mdpi.com/2073-4441/11/5/910 |
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doaj-4d3d2769372a41f6856a209a00d971662020-11-25T00:14:41ZengMDPI AGWater2073-44412019-04-0111591010.3390/w11050910w11050910A Brief Review of Random Forests for Water Scientists and Practitioners and Their Recent History in Water ResourcesHristos Tyralis0Georgia Papacharalampous1Andreas Langousis2Air Force Support Command, Hellenic Air Force, Elefsina Air Base, 192 00 Elefsina, GreeceDepartment of Water Resources and Environmental Engineering, School of Civil Engineering, National Technical University of Athens, Iroon Polytechniou 5, 157 80 Zografou, GreeceDepartment of Civil Engineering, School of Engineering, University of Patras, University Campus, Rio, 26 504 Patras, GreeceRandom 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.https://www.mdpi.com/2073-4441/11/5/910classificationdata-drivenhydrological modelinghydrologymachine learningpredictionquantile regression forestssupervised learningvariable importance metrics |
| collection |
DOAJ |
| language |
English |
| format |
Article |
| sources |
DOAJ |
| author |
Hristos Tyralis Georgia Papacharalampous Andreas Langousis |
| spellingShingle |
Hristos Tyralis Georgia Papacharalampous Andreas Langousis A Brief Review of Random Forests for Water Scientists and Practitioners and Their Recent History in Water Resources Water classification data-driven hydrological modeling hydrology machine learning prediction quantile regression forests supervised learning variable importance metrics |
| author_facet |
Hristos Tyralis Georgia Papacharalampous Andreas Langousis |
| author_sort |
Hristos Tyralis |
| title |
A Brief Review of Random Forests for Water Scientists and Practitioners and Their Recent History in Water Resources |
| title_short |
A Brief Review of Random Forests for Water Scientists and Practitioners and Their Recent History in Water Resources |
| title_full |
A Brief Review of Random Forests for Water Scientists and Practitioners and Their Recent History in Water Resources |
| title_fullStr |
A Brief Review of Random Forests for Water Scientists and Practitioners and Their Recent History in Water Resources |
| title_full_unstemmed |
A Brief Review of Random Forests for Water Scientists and Practitioners and Their Recent History in Water Resources |
| title_sort |
brief review of random forests for water scientists and practitioners and their recent history in water resources |
| publisher |
MDPI AG |
| series |
Water |
| issn |
2073-4441 |
| publishDate |
2019-04-01 |
| description |
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. |
| topic |
classification data-driven hydrological modeling hydrology machine learning prediction quantile regression forests supervised learning variable importance metrics |
| url |
https://www.mdpi.com/2073-4441/11/5/910 |
| work_keys_str_mv |
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