A multi-gene genetic programming model for estimating stress-dependent soil water retention curves

Computational Geosciences

Volumn 18, Issue 1, 2014, Pages 45-56

  Garg, Akhil ^a   Garg, Ankit ^b   Tai, K ^a  

^a NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

^b INDIAN INSTITUTE OF TECHNOLOGY GUWAHATI   (India)

Author keywords

Economical; GPTIPS; LS SVM; Multi gene genetic programming; SDSWRC

Indexed keywords

GENETIC ALGORITHM; NUMERICAL MODEL; REGRESSION ANALYSIS; SOIL WATER; STRESS; SUCTION; WATER RETENTION;

EID: 84896402736     PISSN: 14200597     EISSN: None     Source Type: Journal    
DOI: 10.1007/s10596-013-9381-z     Document Type: Article

References (50)

1. Biddle, P.G.: Tree root damage to buildings. In: Causes, Diagnosis and Remedy, vol. 1. Willowmead, Wantage (1998)
2. Biddle, P.G.: Tree root damage to buildings. In: Patterns of Soil Drying in Proximity to Trees on Clay Soils, vol. 2. Willowmead, Wantage (1998)
3. Blight, G.E.: Desiccation of a clay by grass, bushes and trees. Geotech. Geol. Eng. 23(6), 697-720 (2005)
4. Fredlund, D.G., Morgenstern, N.R.: Stress state variables for unsaturated soils. J. Geotech. Geoenvironmental Eng. 103, 447-466 (1977). ASCE 12919
5. Li, J., Sun, D.A., Sheng, D., Sloan, S., Fredlund, D.: Preliminary study on soil water characteristics of Maryland clay. In: Yin, Z.Z., Yuan, J.P., Chiu, A.C.F. (eds.) Proceedings of the 3rd Asian Conference on Unsaturated Soils, Nanjing, China, pp. 569-574. Science Press, Beijing (2007)
6. Li, J.Y., Yang, Q., Li, P.Y., Yang, Q.L.: Experimental research on soil-water characteristic curve of remolded residual soils. Electron. J. Geotech. Eng. 14, 1-12 (2009)
7. Nowamooz, H., Masrouri, F.: Relationships between soil fabric and suction cycles in compacted swelling soils. Eng. Geol. 114(3), 444-455 (2010)
8. Malaya, C., Sreedeep, S.: Critical review on the parameters influencing soil-water characteristic curve. J. Irrig. Drainage Eng. 138(1), 55-62 (2012)
9. Elkady, T.Y., Al-Mahbashi, A.M.: Effect of vertical stress on the soil water characteristic curve of highly expansive soils. In: Unsaturated Soils: Research and Applications, pp. 165-172. Springer, Berlin (2012)
10. Mitchell, J.K.: Fundamentals of Soil Behavior. Wiley, New York (1993)
11. Garg, A., Malaya, C., Sreedeep, S.: A study on the influence of soil-water characteristic curve on the seepage modeling of unsaturated soil. In: International Conference on Advances in Concrete, Structural, and Geotechnical Engineering, BITS Pilani (2009)
12. Garg, A., Malaya, C., Sreedeep, S.: A study on the influence of soil-water characteristic curve on the seepage modeling of unsaturated soil. Int. J. Earth Sci. Eng. 3(2), 40-46 (2010)
13. Garg, A., Malaya, C., Sreedeep, S.: Influence of different suction measuring procedures on SWCC and seepage modeling in unsaturated soils. In: Proceedings of 6ICEG (International Congress on Environmental Geotechnics), New Delhi, pp. 1605-1608. Tata McGraw Hill Education Private Limited, New Delhi (2010)
14. Gupta, S., Larson, W.E.: Estimating soil water retention characteristics from particle size distribution, organic matter percent, and bulk density. Water Resour. Res. 15(6), 1633-1635 (1979)
15. Rawls, W.J., Brakensiek, D.L.: Estimation of soil-water retention and hydraulic properties. In: Morel-Seytoux, H.J. (ed.): Unsaturated Flow in HydrologicModeling Theory and Practice, pp. 275-300. Kluwer, Beltsville (1989)
16. Rawls, W.J., Brakensiek, D.L.: Prediction of soil water properties for hydrologic modeling. In: Jones, E.B., Ward, T.J. (eds.): Watershed Management in the Eighties, Proceedings of a Symposium Sponsored by the Committee on Watershed Management, I & D Division, American Society of Civil Engineers, Denver, CO, pp. 293-299. American Society of Civil Engineers, New York (1985)
17. Vereecken, H., Maes, J., Feyen, J., Darius, P.: Estimating the soil moisture retention characteristic from texture, bulk density, and carbon content. Soil Sci. 148(6), 389-403 (1989)
18. Scheinost, A.C., Sinowski, W., Auerswald, K.: Regionalization of soil-water retention curves in a highly variable soilscape, I. Developing a new pedo-transfer function. Geoderma 78, 129-143 (1997)
19. Arya, L.M., Leij, F.J., van Genuchten, M. Th., Shouse, P.J.: Scaling parameter to predict the soil-water characteristic from particle-size distribution data. Soil Sci. Soc. America J. 63, 510-519 (1999)
20. Pachepsky, Y.A., Timlin, D., Varallyay, G.: Artificial neural networks to estimate soil water retention from easily measurable data. Soil Sci. Society America J. 60(3), 727-733 (1996)
21. Pachepsky, Y.A., Rawls, W.J.: Development of Pedotransfer Functions in Soil Hydrology, vol. 30, pp. 1-512. Elsevier, Amsterdam (2004)
22. Schaap, M.G., Bouten, W.: Modeling water retention curves of sandy soils using neural networks. Water Resour. Res. 32(10), 3033-3040 (1996)
23. Schaap, M.G., Leij, F.J.: Using neural networks to predict soil water retention and soil hydraulic conductivity. Soil Tillage Res. 47(1), 37-42 (1998)
24. Schaap, M.G., Leij, F.J.: Improved prediction of unsaturated hydraulic conductivity with the Mualem-van Genuchten model. Soil Sci. Soc. Am. J. 64(3), 843-851 (2000)
25. Tamari, S., Wösten, J.H.M., Ruiz-Suarez, J.C.: Testing an artificial neural network for predicting soil hydraulic conductivity. Soil Sci. Soc. Am. J. 60(6), 1732-1741 (1996)
26. Minasny, B., McBratney, A.B.: A conditioned Latin hypercube method for sampling in the presence of ancillary information. Comput Geosci. 32(9), 1378-1388 (2006)
27. Zhang, W.G., Goh, A.T.C.: Multivariate adaptive regression splines for analysis of geotechnical engineering systems. Comput Geotech. 48, 82-95 (2013)
28. Leij, F.J., Alves, W.J., van Genuchten, M.T., Williams, J.R.: The UNSODA Unsaturated Soil Hydraulic Database, version 1.0. EPA Report, EPA/600, 96, 095. US Environmental Protection Agency, Ada, Oklahoma (1996)
29. Tempel, P., Batjes, N.H., van Engelen, V.W.P.: IGBP-DIS Soil Data Set for Pedotransfer Function Development. International Soil Reference and Information Centre, Wageningen (1996)
30. Madár, J., Abonyi, J., Szeifert, F.: Genetic programming for the identification of nonlinear input-output models. Ind. Eng. Chem. Res. 44, 3178-3186 (2005)
31. Wang, B., Yao, M., Zhu, R.: Gene expression programming with multiple chromosomes. Int. J. Model. Identif. Control. 14, 235-241 (2011)
32. Koza, J.R.: Genetic Programming. II: Automatic Discovery of Reusable Programs. MIT, Cambridge (1994)
33. Garg, A., Bhalerao, Y., Tai, K.: Review of empirical modelling techniques for modelling of turning process. Int. J. Modelling, Identification Control. 20(2), 121-129 (2013)
34. Garg, A., Rachmawati L., Tai, K.: Classification-driven model selection approach of genetic programming in modelling of turning process. Int. J. Adv. Manuf. Technol. (2013, in press). doi:10.1007/s00170-013-5103-x
35. Garg, A., Savalani, M.M., Tai, K.: State-of-the-art in empirical modelling of rapid prototyping processes. Rapid Prototyp. J. (2013, in press)
36. Garg, A., Sriram, S., Tai, K.: Empirical analysis of model selection criteria for genetic programming in modeling of time series system. In: Proceedings of 2013 IEEE Conference on Computational Intelligence for Financial Engineering & Economics (CIFEr), Singapore, pp. 84-88 (2013)
37. Garg, A., Tai, K.: Selection of a robust experimental design for the effective modeling of the nonlinear systems using genetic programming. In: Proceedings of 2013 IEEE Symposium on Computational Intelligence and Data mining (CIDM), Singapore, pp. 293-298 (2013)
38. Garg, A., Tai, K.: Comparison of statistical and machine learning methods in modelling of data with multicollinearity. Int. J. Model. Identif. Control. 18(4), 295-312 (2013)
39. Searson, D.P., Leahy, D.E., Willis, M.J.: GPTIPS: an open source genetic programming toolbox for multigene symbolic regression. In: International Multiconference of Engineers And Computer Scientists, pp. 77-80 (2010)
40. Hiden, H.G.: Data-based modelling using genetic programming. PhD Thesis, Dept. Chemical and Process Engineering, University of Newcastle, UK (1998)
41. Hinchliffe, M., Hiden, H., Mckay, B., Willis, M., Tham, M., Barton, G.: Modelling chemical process systems using a multigene genetic programming algorithm. Late breaking paper, GP'96. 624, pp 28-31. Stanford (1996)
42. Cayda, U., Ekici, S.: Support vector machines models for surface roughness prediction in CNC turning of AISI 304 Austenitic stainless steel. J. Intell. Manuf. 23, 639-650 (2012)
43. Gupta, A.K.: Predictive modelling of turning operations using response surface methodology, artificial neural networks and support vector regression. Int. J. Prod. Res. 48, 763-778 (2010)
44. Garg, A., Tai, K., Lee, C.H., Savalani, M.M.: A Hybrid M5-genetic programming approach for ensuring greater trustworthiness of prediction ability in modelling of FDM process. J. Intell. Manuf. (2013). doi:10.1007/s10845-013-0734-1
45. Kecman, V.: Learning And Soft Computing: Support Vector Machines, Neural Networks, And Fuzzy Logic Models. MIT, Cambridge (2001)
46. Pelckmans, K., Suykens, J.A.K., Vangestel, T., De Brabanter, J., Lukas, L., Hamers, B., et al.: LS-SVMlab: AMATLAB/C Toolbox for Least Squares Support Vector Machines. Tutorial. Kuleuven-Esat, Leuven (2002)
47. Salgado, D.R., Alonso, F.J.: An approach based on current and sound signals for in-process tool wear monitoring. Int. J. Mach. Tools Manuf. 47, 2140-2152 (2007)
48. Salgado, D.R., Alonso, F.J., Cambero, I., Marcelo, A.: In-process surface roughness prediction system using cutting vibrations in turning. Int. J. Adv. Manuf. Technol. 43, 40-51 (2009)
49. Ramanan, T.R., Sridharan, R., Shashikant, K.S., Haq, A.N.: An artificial neural network based heuristic for flow shop scheduling problems. J. Intell. Manuf. 22, 279-288 (2011)
50. Tian, Z.: An artificial neural network method for remaining useful life prediction of equipment subject to condition monitoring. J. Intell. Manuf. 23, 227-237 (2012)