Modelling soil water retention using support vector machines with genetic algorithm optimisation

The Scientific World Journal

Volumn 2014, Issue , 2014, Pages

  Lamorski, Krzysztof ^a   Sławiński, Cezary ^a   Moreno, Felix ^b   Barna, Gyöngyi ^c   Skierucha, Wojciech ^a   Arrue, José L ^d  

^a INSTITUTE OF AGROPHYSICS   (Poland)

^b INSTITUTO DE RECURSOS NATURALES Y AGROBIOLOGÍA DE SEVILLA   (Spain)

^c UNIVERSITY OF PANNONIA   (Hungary)

^d CSIC   (Spain)

Author keywords

[No Author keywords available]

Indexed keywords

SOIL WATER; ALUMINUM SILICATE; CLAY; SOIL; WATER;

ARTICLE; BULK DENSITY; CONTROLLED STUDY; GENETIC ALGORITHM; HYDRAULIC CONDUCTIVITY; PEDOTRANSFER FUNCTION; POROSITY; PREDICTION; SOIL WATER CONTENT; SOIL WATER POTENTIAL; SUPPORT VECTOR MACHINE; WATER RETENTION; ALGORITHM; CHEMISTRY; METABOLISM; SOIL; THEORETICAL MODEL;

ALGORITHMS; ALUMINUM SILICATES; MODELS, THEORETICAL; POROSITY; SOIL; SUPPORT VECTOR MACHINES; WATER;

EID: 84897491940     PISSN: None     EISSN: 1537744X     Source Type: Journal    
DOI: 10.1155/2014/740521     Document Type: Article

References (31)

1. Richards L. A., Porous plate apparatus for measuring moisture retention and transmission. Soil Science 1948 66 2 105 110 10.1097/00010694-194808000- 00003
2. Sławiński C., Walczak R. T., Skierucha W., Error analysis of water conductivity coefficient measurement by instantaneous profiles method. International Agrophysics 2006 20 1 55 61 2-s2.0-30344459397 (Pubitemid 43066095)
3. Wösten J. H. M., Pachepsky Y. A., Rawls W. J., Pedotransfer functions: bridging the gap between available basic soil data and missing soil hydraulic characteristics. Journal of Hydrology 2001 251 3-4 123 150 2-s2.0-0035479883 10.1016/S0022-1694(01)00464-4 (Pubitemid 32792748)
4. van Genuchten M. T., A closed-form equation for predicting the hydraulic conductivity of unsaturated soils. Soil Science Society of America Journal 1980 44 5 892 898 2-s2.0-0019057216 10.2136/sssaj1980.03615995004400050002x
5. Haghverdi A., Cornelis W. M., Ghahraman B., A pseudo-continuous neural network approach for developing water retention pedotransfer functions with limited data. Journal of Hydrology 2012 442-443 46 54 2-s2.0-84860134767 10.1016/j.jhydrol.2012.03.036
6. Ahuja L. R., Naney J. W., Williams R. D., Estimating soil water characteristics from simpler properties or limited data. Soil Science Society of America Journal 1985 49 5 1100 1105 2-s2.0-0022264342
7. Walczak R., Witkowska-Walczak B., Sławiński C., Comparison of correlation models for the estimation of the water retention characteristics of soil. International Agrophysics 2001 16 1 79 82 2-s2.0-0035736818 (Pubitemid 34468273)
8. Walczak R. T., Moreno F., Sławiński C., Fernandez E., Arrue J. L., Modeling of soil water retention curve using soil solid phase parameters. Journal of Hydrology 2006 329 3-4 527 533 2-s2.0-33748941252 10.1016/j.jhydrol.2006.03.005 (Pubitemid 44436236)
9. Wösten J. H. M., Lilly A., Nemes A., le Bas C., Development and use of a database of hydraulic properties of European soils. Geoderma 1999 90 3-4 169 185 2-s2.0-0344731425 10.1016/S0016-7061(98)00132-3 (Pubitemid 29350902)
10. Gupta S. C., Larson W. E., Estimating soil water retention characteristics from particle size distribution, organic matter percent, and bulk density. Water Resources Research 1979 15 6 1633 1635 2-s2.0-0018675429 (Pubitemid 10256375)
11. Rawles W. J., Brakensiek D. L., Estimating soil water retention from soil properties. Journal of the Irrigation & Drainage Division 1982 108 2 166 171 2-s2.0-0019901832
12. Ghanbarian-Alavijeh B., Millán H., Point pedotransfer functions for estimating soil water retention curve. International Agrophysics 2010 24 3 243 251 2-s2.0-79951498923
13. Bayat H., Neyshabouri M. R., Mohammadi K., Nariman-Zadeh N., Estimating water retention with pedotransfer functions using multi-objective group method of data handling and ANNs. Pedosphere 2011 21 1 107 114 2-s2.0-78650660442 10.1016/S1002-0160(10)60085-9
14. Baker L., Ellison D., Optimisation of pedotransfer functions using an artificial neural network ensemble method. Geoderma 2008 144 1-2 212 224 2-s2.0-39549091893 10.1016/j.geoderma.2007.11.016
15. Merdun H., Çinar Ö., Meral R., Apan M., Comparison of artificial neural network and regression pedotransfer functions for prediction of soil water retention and saturated hydraulic conductivity. Soil and Tillage Research 2006 90 1-2 108 116 2-s2.0-33746893825 10.1016/j.still.2005.08.011 (Pubitemid 44189522)
16. Minasny B., McBratney A. B., The neuro-m method for fitting neural network parametric pedotransfer functions. Soil Science Society of America Journal 2002 66 2 352 361 2-s2.0-0036124445 (Pubitemid 34227824)
17. Pachepsky Y. A., Timlin D., Varallyay G., Artificial neural networks to estimate soil water retention from easily measurable data. Soil Science Society of America Journal 1996 60 3 727 733 2-s2.0-0030473352
18. Schaap M. G., Leij F. J., Using neural networks to predict soil water retention and soil hydraulic conductivity. Soil and Tillage Research 1998 47 1-2 37 42 2-s2.0-0032474481 10.1016/S0167-1987(98)00070-1 (Pubitemid 28324637)
19. Burman P., A comparative study of ordinary cross-validation, v-fold cross-validation and the repeated learning-testing methods. Biometrika 1989 76 3 503 514 2-s2.0-0000354976 10.1093/biomet/76.3.503
20. Lamorski K., Pachepsky Y., Sławiński C., Walczak R. T., Using support vector machines to develop pedotransfer functions for water retention of soils in Poland. Soil Science Society of America Journal 2008 72 5 1243 1247 2-s2.0-51149118683 10.2136/sssaj2007.0280N
21. Twarakavi N. K. C., Šimůnek J., Schaap M. G., Development of pedotransfer functions for estimation of soil hydraulic parameters using support vector machines. Soil Science Society of America Journal 2009 73 5 1443 1452 2-s2.0-69949173222 10.2136/sssaj2008.0021
22. Bieganowski A., Witkowska-Walczak B., Gliński J., Sokołowska Z., Sławiński C., Brzezińska M., Włodarczyk T., Database of Polish arable mineral soils: a review. International Agrophysics 2013 27 3 335 350 10.2478/intag-2013-0003
23. Kecman V., Learning and Soft Computing, Support Vector Machines, Neural Networks, and Fuzzy Logic Models 2001 Cambridge, Mass, USA MIT Press
24. Vapnik V. N., The Nature of Statistical Learning Theory 1995 New York, NY, USA Springer
25. Hastie T., Tibshirani R., Friedman J., The Elements of Statistical Learning: Data Mining, Inference and Prediction 2008 2nd New York, NY, USA Springer
26. Schölkopf B., Smola A. J., Williamson R. C., Bartlett P. L., New support vector algorithms. Neural Computation 2000 12 5 1207 1245 2-s2.0-17444438778
27. Wohlberg B., Tartakovsky D. M., Guadagnini A., Subsurface characterization with support vector machines. IEEE Transactions on Geoscience and Remote Sensing 2006 44 1 47 57 2-s2.0-31344434208 10.1109/TGRS.2005.859953 (Pubitemid 43146067)
28. Haupt R. L., Haupt S. E., Practical Genetic Algorithms 2004 Hoboken, NJ, USA John Wiley & Sons
29. Morris M., Factorial sampling plans for preliminary computational experiments. Technometrics 1991 33 2 161 174
30. Campolongo F., Cariboni J., Saltelli A., An effective screening design for sensitivity analysis of large models. Environmental Modelling and Software 2007 22 10 1509 1518 2-s2.0-34147165083 10.1016/j.envsoft.2006.10.004 (Pubitemid 46561424)
31. Cherkassky V., Ma Y., Practical selection of SVM parameters and noise estimation for SVM regression. Neural Networks 2004 17 1 113 126 2-s2.0-0346250790 10.1016/S0893-6080(03)00169-2 (Pubitemid 38019003)