A comparative assessment of support vector regression, artificial neural networks, and random forests for predicting and mapping soil organic carbon stocks across an Afromontane landscape.

By K. Were, D. Tien Bui, Ø. Dick, Bal R Singh. 2015

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Landslide susceptibility modelling using different advanced decision trees methods (2018, background): “stocks (Were et al. 2015), and aboveground biomass (Vafaei et al.”
Recent Advances of Hyperspectral Imaging Technology and Applications in Agriculture (2020, background): “classifying different ground covers [36,114,187].”
A modeling study with an artificial neural network: developing estimationmodels for the tomato plant leaf area (2016, result): “Similar results were reported in many ANN studies in the field of agriculture (Liu et al., 2010; Vazquez-Cruz et al., 2012; Khoshnevisan et al., 2014; Guine, 2015; Were et al., 2015).”
Machine learning and soil sciences: a review aided by machine learning tools (2019, background): “For instance, RF is mostly considered in the category of low interpretability (Brungard et al., 2015; Were et al., 2015; Taghizadeh-Mehrjardi et al., 2016; Deng et al., 2018), but its use is also sometimes justified due to its ease of interpretability via the use of variables of importance (Jeong et”
Remote Sensing Techniques for Soil Organic Carbon Estimation: A Review (2019, background): “4% per year would be considerably beneficial for reduction of GHG emissions [4].”