Random forests as a tool for ecohydrological distribution modelling

By Jan Peters, B. De Baets, Niko E. C. Verhoest, R. Samson, Sven Degroeve, P. Becker, W. Huybrechts. 2007

387 citations recorded by the source corpus.

Subfields: Ecological Modeling, Global and Planetary Change, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/7b76f429d228b397d2b653b9a390790d9eebcc4e

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In Ecological Modeling

In Global and Planetary Change

In Nature and Landscape Conservation

Citations in the corpus

Machine learning predictive models for mineral prospectivity: an evaluation of neural networks, random forest, regression trees and support vector machines (2015, background): “These oob elements can be used by the k-thtree to evaluate performance (Peters et al., 2007).”
GIS-based groundwater potential mapping using boosted regression tree, classification and regression tree, and random forest machine learning models in Iran (2015, result): “Although previous studies have noted superior performance for the RF method (Peters et al. 2007; Vorpahl et al. 2012; Oliveira et al. 2012), the results from this study showed that the RF method had the worst performance in groundwater spring potential mapping.”
Updating soil survey maps using random forest and conditioned Latin hypercube sampling in the loess derived soils of northern Iran (2014, methodology): “Those observations not in the bootstrap sample are referred to as out-ofbag (OOB) and the proportion of misclassification of these samples (OOB error) can be used as a measure of generalization error (Peters et al., 2007).”
Incorporating limited field operability and legacy soil samples in a hypercube sampling design for digital soil mapping (2016, neutral): “This is accomplished by (1) setting up each tree based on a bootstrap sample of the respective soil sample set, and by (2) identifying the best split predictor covariate at each tree node from a random subset ( Peters et al., 2007).”
Overview of random forest methodology and practical guidance with emphasis on computational biology and bioinformatics (2012, background): “Garz´on 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.”
Improving protein fold recognition by random forest (2014, background): “Random forest has been used extensively in a wide variety of domains [12-14] including protein fold classification [15-17], which is related to, but different than protein fold recognition.”
Mining data with random forests: A survey and results of new tests (2011, background): “RF have been used for large variety of tasks, including: identification of DNA-binding proteins [13], segmentation of video objects [14], classification of hyper-spectral data [15,16], prediction of the vegetation type occurrence in Belgian lowland valley based on spatially distributed measurements ”