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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Bibliographic Details
Published in:Water (Basel) Vol. 11; no. 5; p. 910
Main Authors: Tyralis, Hristos, Papacharalampous, Georgia, Langousis, Andreas
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.05.2019
Subjects:
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
ISSN:2073-4441, 2073-4441
Online Access:Get full text
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Summary: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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ISSN:2073-4441
2073-4441
DOI:10.3390/w11050910