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A Brief Review of Random Forests for Water Scientists and Practitioners and Their Recent History in Water Resources

1
Air Force Support Command, Hellenic Air Force, Elefsina Air Base, 192 00 Elefsina, Greece
2
Department of Water Resources and Environmental Engineering, School of Civil Engineering, National Technical University of Athens, Iroon Polytechniou 5, 157 80 Zografou, Greece
3
Department of Civil Engineering, School of Engineering, University of Patras, University Campus, Rio, 26 504 Patras, Greece
*
Author to whom correspondence should be addressed.
Water 2019, 11(5), 910; https://doi.org/10.3390/w11050910
Received: 26 March 2019 / Revised: 25 April 2019 / Accepted: 26 April 2019 / Published: 30 April 2019
(This article belongs to the Special Issue Techniques for Mapping and Assessing Surface Runoff)
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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. View Full-Text
Keywords: classification; data-driven; hydrological modeling; hydrology; machine learning; prediction; quantile regression forests; supervised learning; variable importance metrics classification; data-driven; hydrological modeling; hydrology; machine learning; prediction; quantile regression forests; supervised learning; variable importance metrics
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Tyralis, H.; Papacharalampous, G.; Langousis, A. A Brief Review of Random Forests for Water Scientists and Practitioners and Their Recent History in Water Resources. Water 2019, 11, 910.

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