Neural computing models for prediction of permeability coefficient of coarse-grained soils

Neural Computing and Applications

Volumn 21, Issue 5, 2012, Pages 957-968

  Yilmaz, Işik ^a   Marschalko, Marian ^b   Bednarik, Martin ^c   Kaynar, Oǧuz ^a   Fojtova, Lucie ^b  

^a CUMHURIYET UNIVERSITY   (Turkey)

^b VSB TECHNICAL UNIVERSITY OF OSTRAVA   (Czech Republic)

^c Physics and Informatics   (Slovakia)

Author keywords

ANFIS; ANN; Grain size; MLP; Permeability; RBF; Soft computing; Soils

Indexed keywords

ANFIS; ANN; GRAIN SIZE; MLP; RBF;

FUZZY SYSTEMS; HYDRAULIC CONDUCTIVITY; MECHANICAL PERMEABILITY; NEURAL NETWORKS; RADIAL BASIS FUNCTION NETWORKS; SOFT COMPUTING; SOIL MECHANICS; SOILS; STATISTICAL MECHANICS;

FORECASTING;

EID: 84865616327     PISSN: 09410643     EISSN: None     Source Type: Journal    
DOI: 10.1007/s00521-011-0535-4     Document Type: Article

References (46)

1. Shepherd RG (1989) Correlations of permeability and grain size. Ground Water 27(5): 633-638.
2. Yilmaz I (2006) Indirect estimation of the swelling percent and a new classification of soils depending on liquid limit and cation exchange capacity. Eng Geol 85: 295-301.
3. Alyamani MS, Sen Z (1993) Determination of hydraulic conductivity from complete grain-size distribution curves. Ground Water 31(4): 551-555.
4. Hazen A (1892) Some physical properties of sands and gravels. Massachusetts State Board of Health, Annual Report, pp 539-556.
5. Uma KO, Egboka BCE, Onuoha KM (1989) New statistical grain-size method for evaluating the hydraulic conductivity of sandy aquifers. J Hydrol 108: 343-366.
6. Krumbein WC, Monk GD (1943) Permeability as a function of the size parameters of unconsolidated sands. Trans Am Inst Min Metall Petroleum Eng 151: 153-163.
7. Kozeny J (1953) Das wasser in boden, grundwasserbewegung. Hydraulik 280-445.
8. Rawls WJ, Brakensiek DL (1989) Estimation of soil water retention and hydraulic properties. In: Morel-Seytoux HJ (ed) Unsaturated flow in hydrologic modeling theory and practice. Kluwer, Dordrecht, pp 275-300.
9. Jabro JD (1992) Estimation of saturated hydraulic conductivity of soils from particle size distribution and bulk density data. J Am Soc Agric Eng 35(2): 557-560.
10. Sperry JM, Peirce JJ (1995) A model for estimating the hydraulic conductivity of granular material based on grain shape, grain size, and porosity. Ground Water 33(6): 892-898.
11. Lebron I, Schaap MG, Suarez DL (1999) Saturated hydraulic conductivity prediction from microscopic pore geometry measurements and neural network analysis. Water Resour Res 35(10): 3149-3158.
12. Cronican AE, Gribb MM (2007). Literature review: equations for predicting hydraulic conductivity based on grain-size data. (Supplement to Technical Note entitled: Hydraulic conductivity prediction for sandy soils published in Groundwater 42(3): 459-464, 2004). http://coen. boisestate. edu/datedmaterial/images/LITERATURE_REVIEW-final. pdf.
13. ASTM (1990) Sec. 4, Vol. 04. 08, soil and rock; dimension stone; geosynthetics. American Society for Testing and Materials, Designation, pp D 421, D 422.
14. ASTM (2006) Standard test method for permeability for granular soils. American Society for Testing and Materials, Designation, pp D 4234-D 4268.
15. Jin Y, Jiang J (1999) Techniques in neural-network based fuzzy system identification and their application to control of complex systems. In: Leondes CT (ed) Fuzzy theory systems, techniques and applications. Academic Press, New York, pp 112-128.
16. Kaynar O, Yilmaz I, Demirkoparan F (2011) Forecasting of natural gas consumption with neural network and neuro fuzzy system. Energy Educ Sci Technol A Energy Sci Res 26: 221-238.
17. Singh TN, Kanchan R, Verma AK, Singh S (2003) An intelligent approach for prediction of triaxial properties using unconfined uniaxial strength. Min Eng J 5: 12-16.
18. Yilmaz I (2009) Landslide susceptibility mapping using frequency ratio, logistic regression, artificial neural networks and their comparison: a case study from Kat landslides (Tokat-Turkey). Comput Geosci 35(6): 1125-1138.
19. Yilmaz I (2009) Comparison of landslide susceptibility mapping methodologies for Koyulhisar, Turkey: conditional probability, logistic regression, artificial neural networks, and support vector machine. Environ Earth Sci 61(4): 821-836.
20. Rumelhart D, McClelland J (1986) Parallel distributed processing: explorations in the microstructure of cognition. Bradford books, MIT Press, Cambridge.
21. Yilmaz I (2010) The effect of the sampling strategies on the landslide susceptibility mapping by Conditional Probability (CP) and Artificial Neural Networks (ANN). Environ Earth Sci 60(3): 505-519.
22. Park J, Sandberg IW (1993) Approximation and radial basis function networks. Neural Comput 5: 305-316.
23. Broomhead DS, Lowe D (1988) Multivariable functional interpolation and adaptive networks. Complex Syst 2: 321-355.
24. Moody J, Darken CJ (1989) Fast learning in networks of locally-tunes processing units. Neural Comput 1(2): 281-294.
25. Park J, Sandberg IW (1991) Universal approximations using radial-basis-function network. Neural Comput 3(2): 246-257.
26. Xu K, Xie M, Tang LC, Ho SL (2003) Application of neural networks in forecasting engine systems reliability. Appl Soft Comput 2: 255-268.
27. Chen S, Cowan CFN, Grant PM (1991) Orthogonal least squares learning algorithm for radial basis function networks. IEEE Trans Neural Netw 2(2): 302-309.
28. Bianchini M, Frasconi P, Gori M (1995) Learning without local minima in radial basis function networks. IEEE Trans Neural Netw 6(3): 749-755.
29. Sheta AF, Jong KD (2001) Time-series forecasting using GA-tuned radial basis function. Inf Sci 133: 221-228.
30. Foody GM (2004) Supervised image classification by MLP and RBF neural networks with and without an exhaustively defined set of classes. Int J Remote Sens 25(15): 3091-3104.
31. Rivas VM, Merelo JJ, Castillo PA, Arenas MG, Castellano JG (2004) Evolving RBF neural networks for time-series forecasting with EvRBF. Inf Sci 165: 207-220.
32. Harpham C, Dawson CW (2006) The effect of different basis functions on a radial basis function network for time series prediction: a comparative study. Neurocomputing 69: 2161-2170.
33. Sarimveis H, Doganis P, Alexandridis AA (2006) Classification technique based on radial basis function neural networks. Adv Eng Softw 37: 218-221.
34. Zhang R, Huang G, Sundararajan N, Saratchandran P (2007) Improved GAP-RBF network for classification problems. Neurocomputing 70: 3011-3018.
35. Yu L, Lai KK, Wang S (2008) Multistage RBF neural network ensemble learning for exchange rates forecasting. Neurocomputing 71: 3295-3302.
36. Matlab 7. 1 (2005) Software for technical computing and model-based design. The MathWorks Inc.
37. Alvarez GM, Babuska R (1999) Fuzzy model for the prediction of unconfined compressive strength of rock samples. Int J Rock Mech Min Sci 36: 339-349.
38. Finol J, Guo YK, Jing XD (2001) A rule based fuzzy model for the prediction of petrophysical rock parameters. L Petr Sci Eng 29: 97-113.
39. Yilmaz I, Yüksek AG (2008) An example of artificial neural network application for indirect estimation of rock parameters. Rock Mech Rock Eng 41: 781-795.
40. Yilmaz I, Yüksek AG (2009) Prediction of the strength and elasticity modulus of gypsum using multiple regression, ANN, ANFIS models and their comparison. Int J Rock Mech Min Sci 46(4): 803-810.
41. Jang JSR, Chuen-Tsai S (1995) Neuro-fuzzy modeling and control. Proc IEEE 83: 378-406.
42. Lee CC (1990) Fuzzy logic in control systems: fuzzy logic controller. I. IEEE Trans Syst Man Cybern 20: 404-418.
43. Lee CC (1990) Fuzzy logic in control systems: fuzzy logic controller. II. IEEE Trans Syst Man Cybern 20: 419-435.
44. Jang JR (1993) ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern 23: 665-685.
45. Loukas YL (2001) Adaptive neuro-fuzzy inference system: an instant and architecture-free predictor for improved QSAR studies. J Med Chem 44: 2772-2783.
46. SPSS 10. 0. 1 (1999) Statistical analysis software (Standard Version). SPSS Inc.