Use of the Nonparametric Nearest Neighbor Approach to Estimate Soil Hydraulic Properties

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A pseudo-continuous neural network approach for developing water retention pedotransfer functions with limited data (2012, methodology): “Nemes et al. (2006a) showed that the improvement when using local data is related to the actual soil properties and is not origin specific.”
Prediction of Water Retention of Soils from the Humid Tropics by the Nonparametric k‐Nearest Neighbor Approach (2013, methodology): “In this study, we reoptimized the two parameters using an approach similar to the one used by Nemes et al. (2006a).”
Using Pedotransfer Functions to Estimate the van Genuchten–Mualem Soil Hydraulic Properties: A Review (2010, methodology): “Techniques such as nearest neighbor methods (e.g., Nemes et al., 2006a,b) and support vector machine approaches (e.g., Twarakavi et al., 2009) based on machine learning methods seem to be very promising in this respect.”
Development of Pedotransfer Functions for Estimation of Soil Hydraulic Parameters using Support Vector Machines (2009, methodology): “More sophisticated methods use various kinds of ANNs (Pachepsky et al., 1996; Tamari et al., 1996; Minasny et al., 1999), Group Method of Data Handling (Pachepsky and Rawls, 1999), or make use of multi-dimensional nearest neighbor techniques (Nemes et al., 2006a, 2006b).”