Agyare, W. A., Park, S. J. and Vlek, P. L. G. 2007. Artificial neural network estimation of saturated hydraulic conductivity. Vadose Zone J. 6(2): 423-431.
Amir-Abedi, H. 2012. Estimation soil hydraulic properties of Ardabil plain using artificial neural networks and regression methods. M. Sc. Thesis. Faculty of Agricultural Technology and Natural Resources. University of Mohaghegh Ardabili. (in Persian)
Amjadi, N. 2002. Introduction to Intelligent Systems. Semnan University Press. (in Persian)
Anon. 2008. MATLAB software help manual. Version7.6.0.2008b. The Math Works, Inc.
Aqil, M., Kita, I., Yano, A. and Nishiyama, S. 2007. A comparative study of artificial neural networks
and neuro-fuzzy in continuous modeling of the daily and hourly behaviour of runoff. J. Hydrol.
337(1-2): 22-34.
Demuth, H. and Beale, M. 2000. Neural network toolbox user Guide. Copyright 1992-2002. By The Math Works, Inc. Version4.
Finol, J., Guo, Y. K. and Jing, X. D. 2001. A rule based fuzzy model for the prediction of petrophysical rock parameters. J. Petrol. Sci. Eng. 29, 97-113.
Firoozi, S., Sheikhdavoodi, M. J. and Sami, M. 2014. Evaluation the ability of different artificial
intelligence-based modeling techniques in prediction of yield using energy inputs data of farms. J. life Sci. Biomed. 4(3): 214-220.
Givi, J., Prasherb, S. O. and Patel, R. M. 2004. Evaluation of pedotransfer functions in predicting the soil water contents at field capacity and wilting point. Agri. Water Manage. 70, 83-96.
Gokceoglu, C. 2002. A fuzzy triangular chart to predict the uniaxial compressive strength of the Ankara agglomerates from their petrographic composition. Eng. Geol. 66, 39-51.
Heusher, S. A., Brandt, C. C. and Jardin, P. M. 2005. Using soil physical and chemical properties to estimate bulk density. Soil Sci. Soc. Am. J. 69, 51-56.
Jacovides, C. P. 1997. Reply to comment on Statistical procedures for the evaluation of evapotranspiration models. Agr. Water Manage. 3, 95-97.
Jang, J. S. R. 1993. ANFIS: Adaptive Network Based Fuzzy Inference System. IEEE Trans. Syst. Man Cybern. 23 (3): 665-685.
Jang, J. S. R. and Sun, C. T. 1997. Neuro-fuzzy modeling and control. Proceedings of the IEEE. 83(3):
378-406.
Karakus, M. and Tutmez. B. 2006. Fuzzy and multiple regression modeling for evaluation of intact rock strength based on point load, Schmidt hammer and sonic velocity, Rock Mech. Rock Eng.
39(1): 45-57.
Karatalopoulos, S. V. 2000. Understanding Neural Networks and Fuzzy Logic-Basic Concepts and Applications. Wiley-IEEE Press. New-Delhi. India.
Kianpur-Kalkhajeh, U., Rezaie-Arshad, R., Amerikhah, H. and Sami. M. 2010. Multiple linear regression, artificial neural network (MLP, RBF) and ANFIS models for modeling the saturated hydraulic conductivity of tropical region soils (case study: Khuzestan province: sothwest Iran). Int. J. Agr. Res and Rev. 2(3): 255-265.
Lippman, R. P. 1987. An introduction to computing with neural nets. ASSP Magazine, IEEE. 4(2):4-22.
Luk, K. C., Ball, J. E. and Sharma. A. 2000. A study of optimal model lag and spatial inputs to artificial neural network for rainfall forecasting. J. Hydrol. 227(1-4): 56-65.
Marshal, T. J. 1958. A relationship between permeability and size distribution of pores. Soil Sci. 9, 1-8.
Menhaj, M. B. 1998. Principle of Neural Networks. First edition. Professor Hesabi Publishing Center. Tehran. Iran.
Merdun, H., Ozer, C., Meral, R. and Apan, M. 2006. Comparison of Artificial Neural Network and regression pedotransfer functions for prediction of soil water retention and saturated hydraulic conductivity. Soil Till. Res. 90(1-2): 108-116.
Mohammadi, J. 2002. Testing an artificial neural network for predicting soil water retention characteristics from soil physical and chemical properties. 17th WCSS. Thailand. Paper No. 221.
Mohammadi, J. and Taheri, M. 2005. Estimation of pedotransfer function using fuzzy regression. J. Agric. Sci. Technol. 9(2): 51-60. (in Persian)
Naderloo, L., Alimardani, R., Omid, M., Sarmadian, F., Javadikia, P., Yaser Torabi, M. and Alimardani. F. 2012. Application of ANFIS to predict crop yield based on different energy inputs. Measurement. 45, 1406-1413.
Nosrati-Karizak, F., Movahhedi-Naieni, S. A. R., Hezarjaribi, A., Roshani, Gh. A. and Dehghani, A. H. 2012. Using artificial neural networks to estimate the saturated hydraulic conductivity of readily available soil properties. Soil Manage. Sustain. Prod. 2(1): 95-110.
Rahimi-Ajdadi, F. and Abbaspour-Gilandeh. Y. 2011. Artificial neural network and stepwise multiple range regression methods for prediction of tractor fuel consumption. Measurement. 44(10): 2104-2111.
Rawls, W. J. 2004. Pedotransfer functions for the United States. Dev. Soil Sci. 30, 437-447.
Rehman, S. and Mohandes, M. 2008. Artificial neural network estimation of global solar radiation using air temperature and relative humidity. Energ. Policy. 36, 571-576.
Rezaie-Arshad. R. 2010. Expansion pedo transfer functions of soil hydraulic conductivity with artificial neural networks and statistical regression methods for Khouzestan province soils. M.S Thesis. Faculty of Agricultural Science. University of Shahid Chamran. (in Persian)
Salazar, O., Wesstrom, I. and Joel. A. 2008. Evaluation of Drainmod using saturated hydraulic conductivity estimated by a pedotransfer function model. J. Agr. Water Manage. 95, 1135-1143.
Sarmadian, F., Taghizadeh-Mehrjerdi, R., Mohammad-Askari, H. and Akbarzadeh, A. 2010. Comparison of nero fuzzy, neural network and multiple regression methods to predicting some soil properties (Case Study: Golestan province). Iranian J. Soil Water Res. 41(1): 211-220.
Schaap, M. G. and Leij, F. J. 1998. Using neural networkes to predict soil water retention and soil hydraulic conductivity. Soil Till. Res. 47, 37-42.
Soltani, S. and Morid, S. 2002. Comparison of Conceptual model with artificial neural network to simulate rainfall-runoff process. Proceeding of the 6th International River Engineering Conference. Shahid Chamran University. Ahvaz. Iran. (in Persian)
Tamari, S., Wosten, J. H. M. and Ruz-suarez, J. C. 1996. Testing an artificial neural network for predicting soil hydraulic conductivity. Soil Sci. Soc. Am. J. 60, 1732-1741.
Vos, B. D., Meirvenne, M. V., Quataert, P., Deckers, J. and Muys, B. 2005. Predictive quality of pedotransfer functions for estimating bulk density of forest soils. Soil Sci. Soc. Am. J. 69, 500-510.
Wosten, J. H. M., Lilly, A. Nemes, A. and Bas, C. 1999. Development and use of a database of hydraulicproperties of European soils. Geoderma. 90, 169-185.
Yilmaz, I. and Kaynar, O. 2011. Multiple regression, ANN (RBF, MLP) and ANFIS models for prediction of swell potential of clayey soils. Expert Syst. Appl. 38(5): 5958-5966.
Schaap, M. G. and Leij, F. J. 1998. Using neural networkes to predict soil water retention and soil hydraulic conductivity. Soil Till. Res. 47, 37-42.
Soltani, S. and Morid, S. 2002. Comparison of Conceptual model with artificial neural network to simulate rainfall-runoff process. Proceeding of the 6th International River Engineering Conference. Shahid Chamran University. Ahvaz. Iran. (in Persian)
Tamari, S., Wosten, J. H. M. and Ruz-suarez, J. C. 1996. Testing an artificial neural network for predicting soil hydraulic conductivity. Soil Sci. Soc. Am. J. 60, 1732-1741.
Vos, B. D., Meirvenne, M. V., Quataert, P., Deckers, J. and Muys, B. 2005. Predictive quality of pedotransfer functions for estimating bulk density of forest soils. Soil Sci. Soc. Am. J. 69, 500-510.
Wosten, J. H. M., Lilly, A. Nemes, A. and Bas, C. 1999. Development and use of a database of hydraulicproperties of European soils. Geoderma. 90, 169-185.
Yilmaz, I. and Kaynar, O. 2011. Multiple regression, ANN (RBF, MLP) and ANFIS models for prediction of swell potential of clayey soils. Expert Syst. Appl. 38(5): 5958-5966.
)