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In this study, machine learning methods such as neural networks, random forests, and Gaussian processes are applied to the estimation of copper grade in a mineral deposit. The performance of these methods is compared to geostatistical techniques, such as ordinary kriging and indicator kriging. To ensure that these comparisons are realistic and relevant, the predictive accuracy is estimated on test...
Complex, mechanistic hydrological models can be computationally expensive, have large numbers of input parameters, and generate multivariate output. Model emulators can be constructed to approximate these complex models with substantial computational savings, making activities such as sensitivity analysis, calibration and uncertainty analysis feasible. Success in the use of an emulator relies on it...
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