Determination of soil-water retention curve: an artificial intelligence-based approach

Ehsan Nikooee; Rasoul Mirghafari; Ghassem Habibagahi; Alireza Ghadamgahi Khorassani; Amir Mohammad Nouri

doi:10.1051/e3sconf/202019502010

All issues

Volume 195 (2020)

E3S Web Conf., 195 (2020) 02010

References

Open Access

Issue		E3S Web Conf. Volume 195, 2020 4^th European Conference on Unsaturated Soils (E-UNSAT 2020)


Article Number		02010
Number of page(s)		6
Section		Teoretical and Numerical Models
DOI		https://doi.org/10.1051/e3sconf/202019502010
Published online		16 October 2020

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M. Th.Van Genuchten, A closed-form equation for predicting the hydraulic conductivity of unsaturated soils 1. SOIL SCI SOC AM J. 44, 5 (1980). [Google Scholar]
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J. Tomasella, and M. G. Hodnett. Estimating soil water retention characteristics from limited data in Brazilian Amazonia. SOIL SCI. 163, 3 (1998). [Google Scholar]
E. Nikooee, T. Sweijen, and S. Majid Hassanizadeh. Determination of the relationship among capillary pressure, saturation and interfacial area: a pore unit assembly approach. E3S Web of Conferences. Vol. 9. EDP Sciences, 2016.A [CrossRef] [EDP Sciences] [Google Scholar]
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A. Rostami, G. Habibagahi, M. Ajdari, and E. Nikooee, Pore network investigation on hysteresis phenomena and influence of stress state on the SWRC. INT. J. GEOMECH. 15.5 (2013): 04014072.A [Google Scholar]
J. L. Hutson, and A. Cass. A retentivity function for use in soil–water simulation models. J. SOIL SCI. 38,1 (1987). [CrossRef] [Google Scholar]
M. Aubertin, J. F. Ricard, and R. P. Chapuis. A predictive model for the water retention curve: application to tailings from hard-rock mines. CAN. GEOTECH. J. 35,1 (1998). [CrossRef] [Google Scholar]
A. Johari, and A. A. Javadi. Prediction of a soil-water characteristic curve using neural network. 5^th InternationalConference on Unsaturated Soils (2011). [Google Scholar]
A. Johari, G. Habibagahi, and A. Ghahramani. Prediction of soil–water characteristic curve using genetic programming. J. GEOTECH GEOENVIRON 132, 5 (2006). [CrossRef] [Google Scholar]
SoilVision, S. L. SoilVision: a Knowledge-based database system for saturated/unsaturated soil properties, version 4.14. SoilVision Systems Ltd, Saskatchewan (2005). [Google Scholar]
L. M., Arya, and J. F. Paris. A physicoempirical model to predict the soil moisture characteristic from particle-size distribution and bulk density data 1. SOIL SCI SOC AM J. 45, 6 (1981). [Google Scholar]
J.R. Koza, D.E. Goldberg, D.B. Fogel, and R.L. Riolo, eds.. Genetic Programming: Proceedings of the First Annual Conference, 1996. Cambridge Mass: Mit Press, 1996. [Google Scholar]
A. Garg, A. Garg, K. Tai, S. Barontini, and A. Stokes, A computational intelligence-based genetic programming approach for the simulation of soil water retention curves. TRANSPORT POROUS MED. 103, 3 (2014). [CrossRef] [Google Scholar]
D. P. Searson, D. E. Leahy, and M. J. Willis. GPTIPS: an open source genetic programming toolbox for multigene symbolic regression. Proceedings of the International multiconference of engineers and computer scientists. Vol. 1. Hong Kong: IMECS, 2010. [Google Scholar]
U. Buczko, and H. H. Gerke. Evaluation of the Arya-Paris model for estimating water retention characteristics of lignitic mine soils. SOIL SCI. 170,7 (2005). [Google Scholar]
L. M. Arya, F. J. Leij, M. Th. van Genuchten, and P. J. Shouse. Scaling parameter to predict the soil water characteristic from particle-size distribution data. SOIL SCI SOC AM J. 63, 3 (1999). [Google Scholar]

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[1] A. Ahangar-Asr, A. Johari, and A. A. Javadi. An evolutionary approach to modelling the soil–water characteristic curve in unsaturated soils. COMPUT. GEOSCI. 43 (2012). [Google Scholar]

[2] R. Brooks, and T. Corey. Hydraulics properties of porous media. Hydrology Papers, Colorado State University 24 (1964). [Google Scholar]

[3] M. Th.Van Genuchten, A closed-form equation for predicting the hydraulic conductivity of unsaturated soils 1. SOIL SCI SOC AM J. 44, 5 (1980). [Google Scholar]

[4] H. P. Cresswell, and Z. Paydar. Water retention in Australian soils. I. Description and prediction using parametric functions. SOIL RES. 34, 2 (1996). [CrossRef] [Google Scholar]

[5] J. Tomasella, and M. G. Hodnett. Estimating soil water retention characteristics from limited data in Brazilian Amazonia. SOIL SCI. 163, 3 (1998). [Google Scholar]

[6] E. Nikooee, T. Sweijen, and S. Majid Hassanizadeh. Determination of the relationship among capillary pressure, saturation and interfacial area: a pore unit assembly approach. E3S Web of Conferences. Vol. 9. EDP Sciences, 2016.A [CrossRef] [EDP Sciences] [Google Scholar]

[7] H. Khaksar, G. Habibagahi, and E. Nikooee. SWRC modeling in unsaturated soils: a pore network approach. Poromechanics V: Proceedings of the Fifth Biot Conference on Poromechanics. 2013. [Google Scholar]

[8] A. Rostami, G. Habibagahi, M. Ajdari, and E. Nikooee, Pore network investigation on hysteresis phenomena and influence of stress state on the SWRC. INT. J. GEOMECH. 15.5 (2013): 04014072.A [Google Scholar]

[9] J. L. Hutson, and A. Cass. A retentivity function for use in soil–water simulation models. J. SOIL SCI. 38,1 (1987). [CrossRef] [Google Scholar]

[10] M. Aubertin, J. F. Ricard, and R. P. Chapuis. A predictive model for the water retention curve: application to tailings from hard-rock mines. CAN. GEOTECH. J. 35,1 (1998). [CrossRef] [Google Scholar]

[11] A. Johari, and A. A. Javadi. Prediction of a soil-water characteristic curve using neural network. 5^th InternationalConference on Unsaturated Soils (2011). [Google Scholar]

[12] A. Johari, G. Habibagahi, and A. Ghahramani. Prediction of soil–water characteristic curve using genetic programming. J. GEOTECH GEOENVIRON 132, 5 (2006). [CrossRef] [Google Scholar]

[13] SoilVision, S. L. SoilVision: a Knowledge-based database system for saturated/unsaturated soil properties, version 4.14. SoilVision Systems Ltd, Saskatchewan (2005). [Google Scholar]

[14] L. M., Arya, and J. F. Paris. A physicoempirical model to predict the soil moisture characteristic from particle-size distribution and bulk density data 1. SOIL SCI SOC AM J. 45, 6 (1981). [Google Scholar]

[15] J.R. Koza, D.E. Goldberg, D.B. Fogel, and R.L. Riolo, eds.. Genetic Programming: Proceedings of the First Annual Conference, 1996. Cambridge Mass: Mit Press, 1996. [Google Scholar]

[16] A. Garg, A. Garg, K. Tai, S. Barontini, and A. Stokes, A computational intelligence-based genetic programming approach for the simulation of soil water retention curves. TRANSPORT POROUS MED. 103, 3 (2014). [CrossRef] [Google Scholar]

[17] D. P. Searson, D. E. Leahy, and M. J. Willis. GPTIPS: an open source genetic programming toolbox for multigene symbolic regression. Proceedings of the International multiconference of engineers and computer scientists. Vol. 1. Hong Kong: IMECS, 2010. [Google Scholar]

[18] U. Buczko, and H. H. Gerke. Evaluation of the Arya-Paris model for estimating water retention characteristics of lignitic mine soils. SOIL SCI. 170,7 (2005). [Google Scholar]

[19] L. M. Arya, F. J. Leij, M. Th. van Genuchten, and P. J. Shouse. Scaling parameter to predict the soil water characteristic from particle-size distribution data. SOIL SCI SOC AM J. 63, 3 (1999). [Google Scholar]