Using Support Vector Machines to Develop Pedotransfer Functions for Water Retention of Soils in Poland

By K. Lamorski, Yakov A. Pachepsky, C. Sławiński, R. Walczak. 2008

140 citations recorded by the source corpus.

Subfields: Civil and Structural Engineering, Environmental Engineering, Management, Monitoring, Policy and Law

View Paper: https://www.semanticscholar.org/paper/c3ef0d2a1e0090622ab21d745a2f434cbd0bbd28

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In Civil and Structural Engineering

In Environmental Engineering

In Management, Monitoring, Policy and Law

Citations in the corpus

A pseudo-continuous neural network approach for developing water retention pedotransfer functions with limited data (2012, methodology): “Recently some new mathematical methods, such as support vector machines (Lamorski et al., 2008; Twarakavi et al., 2009) and nonparametric nearest neighbor methods (Nemes ll rights reserved. bile: +98 9153023558.”
Using Pedotransfer Functions to Estimate the van Genuchten–Mualem Soil Hydraulic Properties: A Review (2010, methodology): “Lamorski et al. (2008) introduced support vector machines (SVMs), an alternative data-driven method developed in the fi eld of machine learning, to improve the effi ciency of artifi cial neural networks.”
Revisiting the pseudo continuous pedotransfer function concept: impact of data quality and data mining method (2014, background): “Lamorski et al. (2008) reported relative insensitivity in the performance of SVM PTF to the parameter selection.”
Multi-criteria characterization of recent digital soil mapping and modeling approaches (2009, methodology): “…reviewed DSMM studies used a pedotransfer function approach either based on lab-based spectral data (e.g. Wu et al., 2007; Awiti et al., 2008; Vasques et al., 2008) or neural network models (e.g. Lamorski et al., 2008) to predict soil properties without considering SCORPAN factors as covariates.”
Support vector regression-based modeling of cumulative infiltration of sandy soil (2018, methodology): “…process (GP) regression techniques have been emerged as powerful tools in water and environment engineering applications (Minasny and Perfect 2004; Lamorski et al. 2008; Pal et al. 2011; Elbisy 2015; Karandish and Šimůnek 2016; Singh et al. 2017; Tiwari et al. 2017; Sihag et al. 2017c, Sihag et…”
A multiscale multi-permeability poroplasticity model linked by recursive homogenizations and deep learning (2018, unknown): “In the poromechan-509 ics literature [Johari et al., 2006, Lamorski et al., 2008], the hydro-mechanical path-dependent behavior has been enforced by additional input in a feed-forward neural network or support vector machine.”
Enhanced pedotransfer functions with support vector machines to predict water retention of calcareous soil (2016, methodology): “Support vector regression is an attractive machine-learning method that has been successfully applied to predict soil properties (e.g. Lamorski et al., 2008; Twarakavi et al., 2009; Kovačević et al., 2010; Haghverdi et al., 2014).”