Development of Pedotransfer Functions for Estimation of Soil Hydraulic Parameters using Support Vector Machines

By Navin Twarakavi, Jirka Šimůnek, M. G. Schaap. 2009

155 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/d1d69778b12a648de6f2572ba830a7d8099c8038

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Civil and Structural Engineering

1. Wim Cornelis
2. Amir Haghverdi
3. Jirka Šimůnek
4. Navin Twarakavi
5. M. G. Schaap
6. Yakov A. Pachepsky
7. M. T. V. Genuchten
8. B. Ghahraman

In Environmental Engineering

1. Wim Cornelis
2. Jirka Šimůnek
3. Amir Haghverdi
4. Navin Twarakavi
5. M. G. Schaap
6. M. T. V. Genuchten
7. Yakov A. Pachepsky
8. B. Ghahraman

In Management, Monitoring, Policy and Law

1. Wim Cornelis
2. Amir Haghverdi
3. H. Öztürk
4. B. Ghahraman
5. M. Sakai
6. Navin Twarakavi
7. Jirka Šimůnek
8. M. T. V. Genuchten

Citations in the corpus

• Using Pedotransfer Functions to Estimate the van Genuchten–Mualem Soil Hydraulic Properties: A Review (2010, methodology): “Recently, new methods such as nearest neighbor methods (Nemes et al., 2006a,b; Jagtap et al., 2004), support vector machines (Lamorski et al., 2008; Twarakavi et al., 2009), and multiscale Bayesian neural networks (Jana et al., 2007) have been introduced.”
• 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).”
• An objective analysis of the dynamic nature of field capacity (2009, methodology): “[19] Five soil hydraulic parameters (qr, qs, a, n, and Ks) throughout the entire soil textural triangle were estimated using the ROSETTA pedotransfer functions (PTFs), such that all soil textural possibilities (i.e., combinations of sand, silt, and clay percentages) were considered.”
• 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.”
• Revisiting the pseudo continuous pedotransfer function concept: impact of data quality and data mining method (2014, background): “, 2008) or a parametric PTF (Twarakavi et al., 2009).”