Comparing methods for estimating flow duration curves at ungauged sites

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Flow Prediction in Ungauged Catchments Using Probabilistic Random Forests Regionalization and New Statistical Adequacy Tests (2019, methodology): “RF regression has been successfully applied by the ecohydrological community to regionalize flow indices and to explain variations in hydrological patterns (e.g., Booker & Snelder, 2012; Booker, 2013; Peñas, 2013; Snelder et al., 2013).”
The changing role of ecohydrological science in guiding environmental flows (2014, background): “Furthermore, estimates of flow at ungauged sites can be considerably less accurate (Booker and Snelder 2012).”
Machine learning predictive models for mineral prospectivity: an evaluation of neural networks, random forest, regression trees and support vector machines (2015, methodology): “During the past decade, the family of kernel methods such as SVM (Al-Anazi and Gates, 2010; Booker and Snelder, 2012; Chen et al., 2013; Zhao et al., 2012; Zimmermann et al., 2012) and ensembles of trees such as RF (Chan and Paelinckx, 2008; Davis and Robinson, 2012; Ghimire et al.”