Random forests as a tool for ecohydrological distribution modelling

Peters, Jan; Baets, Bernard De; Verhoest, Niko; Samson, Roeland; Degroeve, Sven; Becker, Piet De; Huybrechts, Willy

doi:10.1016/j.ecolmodel.2007.05.011

Cited by 311 publications

(148 citation statements)

References 45 publications

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“…Classification and regression trees (CART) and Random Forest (RF) are both powerful tools for the analysis of complex ecological data that are recognized for their accuracy, efficiency, and robustness over other traditional methods (Breiman et al, 1984; Cutler et al, 2007;Peters et al, 2007;Perdiguero-Alonso et al, 2008). Their structure is non-parametric, and they are able to handle nonlinear relationships well (Breiman et al, 1984;Breiman, 2001).…”

Section: Performance Of Modeling Methods Usedmentioning

confidence: 99%

“…RF, showing performance at the level of boosting and support vector machines, is one of the most successful ensemble methods and an effective tool in prediction. Recently, both of them have been successfully applied in many fields including ecology, bio-informatics, genetics and earth science (remote sensing) (Moisen and Frescino, 2002;Chen and Liu, 2005;Dolan and Parker, 2005;Pal, 2005;Barker et al, 2006;Cutler et al, 2007;De'ath, 2007;Peters et al, 2007;Elith et al, 2008;Perdiguero-Alonso et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

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Predicting assemblages and species richness of endemic fish in the upper Yangtze River

Wang

Lek-Ang

et al. 2010

Science of The Total Environment

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Section: Performance Of Modeling Methods Usedmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Predicting assemblages and species richness of endemic fish in the upper Yangtze River

Wang

Lek-Ang

et al. 2010

Science of The Total Environment

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“…Garzón et al [27], Evans and Cushman [25], Cutler et al [23] and Hernandez et al [35] predict the presence of a species from climatic and topographic variables and Peters et al [60] show that RF performs well in the prediction of vegetation types from environmental variables. Perdiguero-Alonso et al…”

Section: Rf Applications In Bioinformatics: Some Examplesmentioning

confidence: 99%

Overview of random forest methodology and practical guidance with emphasis on computational biology and bioinformatics

Boulesteix

Janitza

Kruppa

et al. 2012

WIREs Data Min & Knowl

571

426

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The Random Forest (RF) algorithm by Leo Breiman has become a standard data analysis tool in bioinformatics. It has shown excellent performance in settings where the number of variables is much larger than the number of observations, can cope with complex interaction structures as well as highly correlated variables and returns measures of variable importance. This paper synthesizes ten years of RF development with emphasis on applications to bioinformatics and computational biology. Special attention is given to practical aspects such as the selection of parameters, available RF implementations, and important pitfalls and biases of RF and its variable importance measures (VIMs). The paper surveys recent developments of the methodology relevant to bioinformatics as well as some representative examples of RF applications in this context and possible directions for future research.

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“…7)." Peters et al (2007) interpret the proportion of trees as a probability. Williams and Abernethy (2008) refer to it as the prediction confidence and suggest that it could be used in fuzzy logic algorithms.…”

Section: Components and Map Unitsmentioning

confidence: 99%

Random Forests Applied as a Soil Spatial Predictive Model in Arid Utah

Stum

2010

Digital Soil Mapping

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Initial soil surveys are incomplete for large tracts of public land in the western USA. Digital soil mapping offers a quantitative approach as an alternative to traditional soil mapping. I sought to predict soil classes across an arid to semiarid watershed of western Utah by applying random forests (RF) and using environmental covariates derived from Landsat 7 Enhanced Thematic Mapper Plus (ETM+) and digital elevation models (DEM). Random forests are similar to classification and regression trees (CART).However, RF is doubly random. Many (e.g., 500) weak trees are grown (trained) independently because each tree is trained with a new randomly selected bootstrap sample, and a random subset of variables is used to split each node. To train and validate the RF trees, 561 soil descriptions were made in the field. An additional 111 points were added by case-based reasoning using aerial photo interpretation. As RF makes classification decisions from the mode of many independently grown trees, model iii uncertainty can be derived. The overall out of the bag (OOB) error was lower without weighting of classes; weighting increased the overall OOB error and the resulting output did not reflect soil-landscape relationships observed in the field. The final RF model had an OOB error of 55.2% and predicted soils on landforms consistent with soil-landscape relationships. The OOB error for individual classes typically decreased with increasing class size. In addition to the final classification, I determined the second and third most likely classification, model confidence, and the hypothetical extent of individual classes.Pixels that had high possibility of belonging to multiple soil classes were aggregated using a minimum confidence value based on limiting soil features, which is an effective and objective method of determining membership in soil map unit associations and complexes mapped at the 1:24,000 scale. Variables derived from both DEM and Landsat 7 ETM+ sources were important for predicting soil classes based on Gini and standard measures of variable importance and OOB errors from groves grown with exclusively DEM-or Landsat-derived data. Random forests was a powerful predictor of soil classes and produced outputs that facilitated further understanding of soil-landscape relationships.(147 pages) iv ACKNOWLEDGMENTS

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Random forests as a tool for ecohydrological distribution modelling

Cited by 311 publications

References 45 publications

Predicting assemblages and species richness of endemic fish in the upper Yangtze River

Predicting assemblages and species richness of endemic fish in the upper Yangtze River

Overview of random forest methodology and practical guidance with emphasis on computational biology and bioinformatics

Random Forests Applied as a Soil Spatial Predictive Model in Arid Utah

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