منابع مورد استفاده
بنایی مح، مؤمنی ع، بای بوردی م و ملکوتی مج، ۱۳۸۳. خاکهای ایران. انتشارات سنا، تهران.
صبح خیزی م، اکبری ع، شتربان ع و شکویی م، 1385. طرح شناخت مناطق اکولوژیک کشور، تیپ‌های گیاهی منطقه زابل. چاپ اول، موسسه تحقیقات جنگل‌ها و مراتع کشور، تهران.
فرهمند ا، اوستان ش، جعفرزاده عا و علی‌اصغرزاد ن، 1391. پارامترهای شوری و سدیمی بودن در برخی خاکهای متأثر از نمک دشت تبریز. نشریه دانش آب و خاک، جلد 22، شماره1، صفحه‌های 1 تا 15.
منهاج م ب، 1386. مبانی شبکه‌های عصبی. جلد 1، دانشگاه صنعتی امیرکبیر، تهران.
نوابیان م، اشرف تالش سح، اسمعیلی ورکی م و جمالی ع، 1390. مقایسه توابع انتقالی رگرسیونی و شبکه عصبی مصنوعی با ANFIS در تخمین هدایت آبی اشباع. دوازدهمین کنگره علوم خاک ایران.12-14 شهریور ماه، دانشگاه تبریز.
Agyare WA and Park SJ, 2007. Artificial neural network estimation of saturated hydraulic conductivity. Vadose Zone Journal 6: 423-431.
Bower CA, Reitemeier RF and Fireman M, 1952. Exchangeable cation analysis of saline and alkali soils. Soil Science 73: 251-261.
Chen SM and Chung NY, 2006. Forecasting enrollments using high-order fuzzy time series and genetic algorithms. International Journal of Intelligent Systems 21: 485-501.
Chi ChM, Zhao ChW, Sun XJ and Wang ZC, 2011. Estimating exchangeable sodium percentage from sodium adsorption ratio of salt-affected soil in The Songnen plain of Northeast China. Soil Science Society of China Pedosphere 21(2): 271-276.
Chung CH, Chiang YM and Chang FJ, 2012. A spatial neural fuzzy network for estimating pan evaporation at ungauged sites. Hydrology and Earth Systems Sciences 16: 255-266.
Emerson WW and Bakker AC, 1973. The comparative effects of exchangeable calcium, magnesium and sodium on some physical Properties of Red-Brown earth sub-soils. II. The spontaneous dispersion of aggregates in water. Australian Journal of Soil Research 11: 151-157.
Endo T, Yamamoto S, Honna T and Eneji AE, 2002. Sodium-Calcium exchange selectivity as influenced by clay minerals and composition. Soil Science 167(2): 117-125.
Erzin Y, Rao BH and Singh D, 2008. Artificial neural network models for predicting soil thermal resistivity. International Journal of Thermal Sciences 47(10):1347-1358.
Evangelou VP and Marsi M, 2003. Influence of ionic strength on sodium-calcium exchange of two temperate climate soils. Plant and Soil 250: 307-313.
Frenkel H and Alperovitch N, 1983. Factors affecting the estimation of exchangeable sodium percentage in soils from Israel. Hassadeh 63: 1291-1296.
Gallant SI, 1993. Neural Network Learning and Expert Systems. MIT Press, USA.
Ghaffari A, Abdollahi H, Khoshayand M, Bozchalooi IS, Dadgar A and Rafiee-Tehrani M, 2006. Performance comparison of neural network training algorithms in modeling of Bimodal Drug Delivery. International Journal of Pharmaceutics 327(1): 126-138.
Jang JSR, 1993. ANFIS–Adaptive-Network-Based Fuzzy inference system. IEEE Transactions on Systems Man and Cybernetics 23: 665-658.
Jorjani E, Chehreh Chelgani S and Mesroghli S, 2008. Application of artificial neural networks to predict chemical desulfurization of Tabas coal. Fuel 87(12): 2727-2734.
Jurinak JJ and Suarez DL. 1990. The chemistry of salt-affected soils, Pp. 42-63. In: Tanji KK (ed). Agricultural Salinity Assessment and Management, No, 71. American Society of Civil Engineers, New York, N.Y.
Kopittke PM, So HB and Menzies NW, 2006. Effect of ionic strength and clay mineralogy on Na-Ca exchange and the SAR-ESP relationship. European Journal of Soil Sciences 57(5): 626-633.
Lake HR, Akbarzadeh A and Mehrjardi RT, 2009. Development of pedotransfer functions (PTFs) to predict soil physico-chemical and hydrological characteristics in southern coastal zones of the Caspian Sea. Journal of Ecology and the Natural Environment 1(7): 160-172.
Levy R. and Hillel D, 1968. Thermodynamic equilibrium constants of sodium-calcium exchange in some Israel soils. Soil Science 106: 393-398.
Marsi M and Evangelou VP, 1991. Chemical and physical behavior of two Kentucky soils: I. Sodium-calcium exchange. Journal of Environmental Science and Health, Part A: Toxic-Hazardous Substances & Environmental Engineering 267: 1147-1176.
Mesquta ME, Goncalves MC, Goncalves AR and Neves MJ, 2005. Effect of electrolyte concentration on sodium adsorption: Application of competitive extended Freundlich isotherms. Arid Land Research and Management 19:161-172.
Minasny B, Hopman J, Harter WT, Eching SO, Toli A and Denton MA, 2004. Neural networks prediction of soil hydraulic functions for alluvial soils using multistep outflow data. Soil Science Society of America Journal 68: 417-429.
Nadler A and Magaritz M, 1981. Expected deviations from the ESP-SAR empirical relationships in calcium and sodium-carbonate-containing arid soils: field evidence. Soil Science 131: 220-225.
Quirk JP, 2001. The significance of the threshold and turbidity concentrations in relation to sodicity and microstructure. Australian Journal of Soil Research 39: 1185-1217.
Rengasamy P, Greene RSB, Ford GW and Mehanni AH, 1984. Identification of dispersive behaviour and the management of red-brown earths. Australian Journal of Soil Research 22: 413-431.
Rhoades JD, 1982. Cation exchange capacity. Pp. 149-157. In: Page AL, Miller RH and Keeney DR (eds). Methods of Soil Analysis. Part 2. Agron. Monogr. 9, American Society of Agronomy, Madison, WI, USA.
Richards LA, 1954. USDA Handbook 60. U.S.Department of Agriculture, Washington DC. USA.
Seilsepour M, Rashidi M and Khabbaz BG, 2009. Prediction of soil exchangeable sodium percentage based on soil sodium adsorption ratio. American-Eurasian Journal of Agriculture Environment Sciences 5(1): 1-4.
Shainberg I, Oster JD and Wood JD, 1980. Sodium-calcium exchange in montmorillonite and illite suspensions. Soil Science Society of American Journal 44: 960-964.
Shu C and Ouarda TBMJ, 2008. Regional flood frequency analysis at ungauged sites using the adaptive neuro-fuzzy inference system. Journal of Hydrology 349: 31-43.
Smith M, 1993. Neural Networks for Statistical Modeling. Thomson Learning, Boston, USA.
Anorymous, 1996. Soil Survey Laboratory Methods Manual. Soil Survey Investigations Rep. 42. Version 3.0. U.S. Gov. Print. Washington DC.
Sumner ME, 1993. Sodic soils: New perspectives. Australian Journal of Soil Research 31: 683-750.
Tamari S, WoÈsten JHM and Ruiz-SuaÂrez JC, 1996. Testing an artificial neural network for predicting soil hydraulic conductivity. Soil Science Society of America Journal 60: 1732-1741.
Torrecilla J, Otero L and Sanz P, 2007. Optimization of an artificial neural network for thermal/pressure food processing: Evaluation of training algorithms. Computers and Electronics in Agriculture 56(2): 101-110.
Wagner B, Tarnawski VR, Hennings V, Muller U, Wessolek G and Plagge R, 2001. Evaluation of pedotransfer function for unsaturated soil hydraulic conductivity using an independent data set. Geoderma 102: 275-297.
Zare M, Ordookhani K, Emadi A and Azarpanah A, 2014. Relationship between soil exchangeable sodium percentage and soil sodium adsorption ratio in Marvdasht plain, Iran. International journal of Advanced Biological and Biomedical Research 2(12): 2934-2939.
)