Modelling Soil Water Retention Using Support Vector Machines with Genetic Algorithm Optimisation

@inproceedings{Lamorski2014ModellingSW,
  title={Modelling Soil Water Retention Using Support Vector Machines with Genetic Algorithm Optimisation},
  author={Krzysztof Lamorski and Cezary Sławiński and Felix Moreno and Gy{\"o}ngyi Barna and Wojciech Skierucha and Jos{\'e} Luis Arr{\'u}e},
  booktitle={TheScientificWorldJournal},
  year={2014}
}
This work presents point pedotransfer function (PTF) models of the soil water retention curve. The developed models allowed for estimation of the soil water content for the specified soil water potentials: -0.98, -3.10, -9.81, -31.02, -491.66, and -1554.78 kPa, based on the following soil characteristics: soil granulometric composition, total porosity, and bulk density. Support Vector Machines (SVM) methodology was used for model development. A new methodology for elaboration of retention… CONTINUE READING

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References

Publications referenced by this paper.
Showing 1-10 of 32 references

Database of Polish arable mineral soils: a review

A. Bieganowski, B. Witkowska-Walczak, J. Gliński
International Agrophysics, vol. 27, no. 3, pp. 335–350, 2013. • 2013
View 1 Excerpt

A pseudocontinuous neural network approach for developing water retention pedotransfer functions with limited data

A. Haghverdi, W. M. Cornelis, B. Ghahraman
Journal of Hydrology, vol. 442-443, pp. 46–54, 2012. • 2012
View 1 Excerpt

Estimatingwater retentionwith pedotransfer functions using multi-objective group method of data handling and ANNs

H. Bayat, M. R. Neyshabouri, K.Mohammadi, andN.Nariman- Zadeh
Pedosphere, vol. 21, no. 1, pp. 107–114, 2011. • 2011

Point pedotransfer functions for estimating soil water retention curve

B. Ghanbarian-Alavijeh, H. Millán
International Agrophysics, vol. 24, no. 3, pp. 243–251, 2010. • 2010
View 1 Excerpt

Optimisation of pedotransfer functions using an artificial neural network ensemble method

L. Baker, D. Ellison
Geoderma, vol. 144, no. 1-2, pp. 212–224, 2008. • 2008

Using support vector machines to develop pedotransfer functions for water retention of soils in Poland

K. Lamorski, Y. Pachepsky, C. Sławiński, R. T. Walczak
Soil Science Society of America Journal, vol. 72, no. 5, pp. 1243–1247, 2008. • 2008
View 3 Excerpts

Comparison of artificial neural network and regression pedotransfer functions for prediction of soil water retention and saturated hydraulic conductivity

H. Merdun, Ö. Çinar, R. Meral, M. Apan
Soil and Tillage Research, vol. 90, no. 1-2, pp. 108– 116, 2006. • 2006

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