Allison LE. 1975. Organic carbon. In: Black CA, Evans DD, White JL, Ensminger LE, Clark FE. (Eds.), Methods of soil analysis, Part 2, Chemical and microbiological properties. American Society of Agronomy, Madison. 1367p.
Auobi Sh and Alizadeh MH. 2006. Soil surface attributes prediction using digital topographic model (Case Study: part of Mehr Watershed, Sabzevar, KhorasanProvince). Science and Technology of Agriculture and Natural Resources, 10 (2): 85-96.
Balan B, Mohaghegh S and Ameri S. 1995. State- of- Art- in permeability determination from well log data: Part 1- A comparative study, Model development. SPE. 30978: 17-25.
Bazartseren B, Hildebrandt G and Holz K. 2003. Short-term water level prediction using neural networks and neuro-fuzzy approach. Neuro computing, 55: 439-450.
Baiat varkesh M, Zare abiane H, Marofi S, Sabziparvar A and Soltani F. 2009. Simulates daily crop reference evapotranspiration using artificial intelligence methods and compared with experimental measurements of having cold semi-arid climate, Hamedan. Journal of Soil and Water Conservation Research, 16 (4): 79-83.
Blake GR and Hartge KH. 1986. Bulk density. P 363-375, In: Klute, A. Methods of soil analysis. Part 1. 2nd Ed. Agron. Monogr. 9. ASA. Madison. WI.
CambardellaCA and Elliott ET. 1992. Particulate soil organic matter changes across a grassland cultivation sequence. American Journal of Soil Science, 56: 777-783.
Caudill M. 1987. Neural networks primer: Part I, AI Expert.
Fajri F. 2009. The report on rangeland & vegetation cover feasibility studies in the kharabeh-sanji basin. Faculty of Natural Resources. University of Urmia.
Gee GW and Bauder JW. 1986. Particle size analysis. 383-411p, In: Methods of soil analysis. Part 1. 2nd Ed. Klute, A. Agron. Monogr. 9. ASA. Madison. WI.
Handayani IP, Coyne MS, Barton C and Workman S. 2008. Soil carbon pools and aggregation follwing land restoration:BernheimForest. Ken. J. Enveron. Monitor. Restor. 4: 11-28.
Haynes RJ. 2005. Labile organic matter fraction as central components of the quality of agricultural soils: An overview. Adv. Agron. 85: 221-268.
Hecht R. 1987. Kolmogorov mapping, neural network existence theorem. 1st IEEE ICNN, 3. Sandiego.
Holmberg M, Forsius M, Starr M and Huttunen M. 2006. An application of artificial neural networks to carbon, nitrogen and phosphorus concentration in three boreal streams and impacts of climate change. International Society for Ecological Information 3rd Conference. Grottaferrata, Roma, Italy. 195: 51-60.
Ingleby HR and Crowe TG. 2001. Neural network models for predicting organic matter content in Saskatchewan soils. Canadian Biosystems Engineering, 43:71-75.
Khanna T. 1990. Foundations of neural networks, Addison-Wesley Pub. Co.USA .
Merdun H, Ozer T, 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 and Tillage Research, 90: 108-116.
Memarian Fard M and Beigi Hrchgany H. 2009. Comparison of artificial neural network models and regression transfer functions to predict soil exchange capacity in Chaharmahal and BakhtiariProvince. Journal of Soil and Water, 4: 90-99.
Moghaddamnia A, Ghafari Gousheh M, Piri J, Amin S and Han D. 2009. Evaporation estimation using artificial neural networks and adaptive neurofuzzy inference system techniques. Advances in Water Resources, 32: 89-97.
Norani V and Salehi K. 2008. Rainfall-runoff modeling using adaptive fuzzy neural network and comparison with neural network and fuzzy inference. National Congress on Civil Engineering, TehranUniversity. Proceedings of the Fourth National Congress of Civil Engineering.
Parasurman K, Elshorbagy A and Si B. 2006. Estimating saturated hydraulic conductivity in spatially variable fields using neural network in Ensembles. SSSA. J. 70: 1851-1859.
Parton WJ, Schmel DS, Cole CV and Ojima DS. 1987. Analysis of factors controlling soil organic matter levels in Great Plains grasslands. A. J. Soil Sci. 51: 1173-1179.
Parsafar NA and Marofi S. 2011. Estimated temperatures at depths using network neural networks Fuzzy (case study: Kermanshah region). J. Soil and Water Sci. 21(3): 21-22.
Pilevari A, Auobi Sh and Khademi H. 2010. Comparison of artificial neural network and multiple linear regression analysis to predict soil organic carbon data to the ground. J. Soil and Water, 24 (6): 1151-1163.
Sabzi parvar A and Beiatorkeshi M. 2010. Assess the accuracy of fuzzy artificial neural network, neurotropic solar radiation simulation. Iranian J. Physic. Res. 4(10): 347-536.
Sinowski W and Auerswald K. 1999. Using relief parameters in a discriminate analysis to stratify geological areas with different spatial variability of soil properties. Geoderma, 89: 113-128.
SkullbergU. 1991. Seasonal Variation of pH h2o and pH cacl2 in centimeter- layers of Mor Humus in a Picea Abies (L.) Karst stand. SwedenUniversity of Agri Sci, Dep.Forest Site Res.
Somaratne S, Seneviratne G and CoomaraswamyU. 2005. Prediction of soil organic carbon across different landuse patterns: A neural network approach. SSSA. J. 69: 1580-1589.
Spaccini R, Mbagwu JC, Igwe CA, Conte P and Piccolo A. 2004. Carbohydrate and aggregation in lowland soil of Nigeria as influenced by organic input. Soil and Tillage Res. 75: 161-172.
Sumﬂeth K and Duttmann R. 2008. Prediction of soil property distribution in paddy soil landscapes using terrain data and satellite information as indicators. Eco. Indicators, 8: 485–501.
Thompson JA and Kolka RK. 2005. Soil carbon storage estimation in a forested watershed using quantitive soil-landscape modeling. SSSA. J. 69: 1086-1093.
Zahedi Gh. 1998. Relation between vegetation and soil characteristics in a mixed hard wood stand. Academic press, GhentUniversity (Belgium), 319p.
Zevebergen LW and Thorne CR. 1987. Quantitative analysis of land surface topography. Earth Surface Processes Landforms, 12: 47-56.