Machine Learning
Machine Learning
Machine Learning
About regression-kriging: From equations to case studies
Computers & Geosciences
Random Forest for Gene Expression Based Cancer Classification: Overlooked Issues
IbPRIA '07 Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part II
Modern Applied Statistics with S
Modern Applied Statistics with S
Modeling airborne benzene in space and time with self-organizing maps and Bayesian techniques
Environmental Modelling & Software
Spatial interpolation of McArthur's Forest Fire Danger Index across Australia: Observational study
Environmental Modelling & Software
Review: Spatial interpolation methods applied in the environmental sciences: A review
Environmental Modelling & Software
Optimizing biodiversity prediction from abiotic parameters
Environmental Modelling & Software
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Machine learning methods, like random forest (RF), have shown their superior performance in various disciplines, but have not been previously applied to the spatial interpolation of environmental variables. In this study, we compared the performance of 23 methods, including RF, support vector machine (SVM), ordinary kriging (OK), inverse distance squared (IDS), and their combinations (i.e., RFOK, RFIDS, SVMOK and SVMIDS), using mud content samples in the southwest Australian margin. We also tested the sensitivity of the combined methods to input variables and the accuracy of averaging predictions of the most accurate methods. The accuracy of the methods was assessed using a 10-fold cross-validation. The spatial patterns of the predictions of the most accurate methods were also visually examined for their validity. This study confirmed the effectiveness of RF, in particular its combination with OK or IDS, and also confirmed the sensitivity of RF and its combined methods to the input variables. Averaging the predictions of the most accurate methods showed no significant improvement in the predictive accuracy. Visual examination proved to be an essential step in assessing the spatial predictions. This study has opened an alternative source of methods for spatial interpolation of environmental properties.