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.
Highlights
Breiman’s [1] random forests (RF) is one of the most successful machine learning algorithms for practical applications; see e.g., Biau and Scornet [2], and Efron and Hastie [3] (p. 324).Despite its practical value, until very recently and compared to other machine learning and artificial intelligence algorithms, random forests remained relatively obscure with limited use in water science and hydrological applications
Until very recently and compared to other machine learning and artificial intelligence algorithms, random forests remained relatively obscure with limited use in water science and hydrological applications
Random forests have been applied to several scientific fields and associated research areas, such as agriculture, ecology, land cover classification, remote sensing, wetland classification, bioinformatics, as well as biological and genetic association studies, genomics, quantitative structure−activity relationships (QSARs) modeling [13], and single nucleotide polymorphism studies (SNP, [14])
Summary
Breiman’s [1] random forests (RF) is one of the most successful machine (statistical) learning algorithms for practical applications; see e.g., Biau and Scornet [2], and Efron and Hastie [3] Until very recently and compared to other machine learning and artificial intelligence algorithms, random forests remained relatively obscure with limited use in water science and hydrological applications. The potential of Breiman’s [1] original algorithm and its variants in water resources applications remain far from fully exploited. Besides common applications of RF-based algorithms in regression and classification problems and computation of relevant metrics, their use for quantile prediction, survival analysis, and causal inference, to name a few, seem to be less known to water scientists and practitioners. An extensive review of the theoretical aspects of random forests can be found
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