1- Andonie R. 2010. Extreme Data Mining: Inference from small data sets, International Journal of Computes Communications and Control, 5(3):280-291.
2- Akbarzadeh A., Taghizadeh Mehrjardi R., Rahimi Lak H., and Ramezanpour H. 2009. Application of artificial intelligence in modeling of soil properties (Case study: Roodbar Region, North of Iran), Environmental Research Journal, 3(2):19-24.
3- Amini M., Abbaspour K.C., Khademi H., Fathianpour N., Afyuni M., and Schulin R. 2005. Neural network models to predict cation exchange capacity in arid regions of Iran, European Journal of Soil Science, 53:748-757.
4- Asgari M.S., Khodadadi M., Sarmadian F., and Gzny R. 2009. The effectiveness of artificial neural networks in the yield of wheat, barley and maize, Journal of Agriculture, 85(1):62-71.
5- Bell M.A., and Van Kulen H. 1995. Soil pedotransfer function for four Mexican soils, Soil Science Society of America Journal, 59:865-871.
6- Carpena O., Lux A., and Vahtras K. 1972. Determination of exchangeable cations in calcareous soils, Journal of Soil Science, 33:194-199.
7- Doran J.W., and Parkin T.B. 1994. Defining and assessing soil quality. p. 543-569. In J.W Doran et al (Ed.) Defining Soil Quality for a Sustainable Environment, Soil Science Society American Special publication, Madison, Wisconsin, USA.
8- DuBose P., and Klimasauskas C. 1989. Introduction to Neural Networks with Examples and Applications, Neural Ware Inc, Pittsburgh.
9- Ghorbani H., Kashi H., Hafezi Moghadas N., and Emamgholizadeh S. 2015. Estimation of soil cation exchange capacity using multiple regression, artificial neural networks and adaptive neuro-fuzzy inference system models in Golestan Province, Iran. Communication in Soil Science and Plant Analysis, 46(6):763-780.
10- Holland J.H. 1984. The Georgians: Genealogies of Pioneer Settlers. Genealogical Publishing Com, Amesterdam.
11- Kashi H., Ghorbani H, Emamgholizadeh S., and Hashemi S.A.A. 2013. The Estimation of Soil Cation Exchange Capacity in Disturbed and Undisturbed Soils Using Artificial Neural Networks and Multiple Regressions, Journal of water and soil, 27:472-484.
12- Kashi H., Emamgholizadeh S., and Ghorbani H. 2014. Estimation of soil infiltration and cation exchange capacity based on multiple regression, ANN (RBF, MLP), and ANFIS Models, Soil Science and Plant Analysis, 45:1195-1213.
13- Keller A., Von steiger B., Van der Zee S.T., and Schulin R. 2001. A stochastic empirical model for regional heavy metal balances in agro ecosystems, Journal of Environmental Quality, 30:1976-1989.
14- Krogh L., Madsen H.B., and Greve M.H. 2000. Cation exchange capacity pedotransfer functions for Danish soils, Soil and Plant Science, 50:1-12.
15- Manrique L.A., Jones C.A., and Dyke P.T. 1991. Predicting cation exchange capacity from soil physical and chemical properties, Soil Science Society of America Journal, 50:787-794.
16- Merdun H., Cinar O., and Apan M. 2006. Comparison of artificial neural network and regression pedotransfer function for prediction of soil water retention and saturated hydraulic conductivity, Soil and Tillage Research, 90:-108-116.
17- McBratney A.B., Minasny B., Cattle S. R., and Vervoort R.W. 2002. From pedotransfer functions to soil inference systems, Geoderma, 109:41-73.
18- Mirzaee S., Ghorbani-dashtaki Sh., Mohammadi J., Asadzadeh F., and Kerry R. 2017. Modeling WEPP erodibility parameters in calcareous soils in northwest Iran, Ecological Indicators, 74:302-310.
19- Oberthur T., Doberman A., and Neue H.V. 1996. How good is a reconnaissance soil map for agronomic purpose?, Soil Use and Management, 12:33-43.
20- Schaap M.G., and Bouten W. 1996. Modelling water retention curves of sandy soils using neural networks, Water Research, 32: 3033-3040.
21- Tang L., Zeng G.M., Nourbakhsh F. and Shen G.L. 2008. Artificial neural network approach for predicting cation exchange capacity in soil based on physico-chemical properties, Environmental Engineering Science, 26(2):1-10.
22- USDA. 2014. Keys to Soil Taxonomy. 12th edition, Soil Survey Staff, Natural Resource Conservation Service.
23- Yang, X. S. 2008. Nature-Inspired Metaheuristic Algorithms, Luniver Press, UK.
24- Yang X.S. 2009. Firefly Algorithms for Multimodal Optimization. P. 169-178. In Stochastic Algorithms. Foundations and Applications, SAGA, Lecture Notes in Computer Sciences, Cambridge, UK.
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