Machine learning predictive models for mineral prospectivity: an evaluation of neural networks, random forest, regression trees and support vector machines

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Short-term prediction of groundwater level using improved random forest regression with a combination of random features (2018, result): “They were also compared on four criteria in mineral prospectivity modeling (Rodriguez-Galiano et al. 2015).”
Predictive modelling for solar thermal energy systems: A comparison of support vector regression, random forest, extra trees and regression trees (2018, methodology): “From the root node of the tree, the data splitting process in each internal node of a rule of the tree is repeated until the stopping criterion is met (Rodriguez-Galiano et al., 2015).”
Selecting critical features for data classification based on machine learning methods (2020, background): “…[51, 52], adjuvant chemotherapy effectiveness assessment in non‑small cell lung cancer [53], and Alzheimer’s disease [54] Finance Mineral prospect [12], Industrial recommendation system [13], financial crisis [55], indus‑ trial coal mine [56], poverty classification [57], spatiotemporal poverty…”
New Ensemble Models for Shallow Landslide Susceptibility Modeling in a Semi-Arid Watershed (2019, background): “Recently, machine learning (ML) techniques have become popular for spatial predictions of natural hazards, including wildﬁre [24], sinkhole development [25], ﬂooding [26–37], drought [38], gully erosion [39,40], earthquakes [41], land / ground subsidence [42], and landslides [4,43–46].”