Document Type : Research Paper
Authors
- Agyare, W.A., and Park, S.J. 2007. Artificial neural network estimation of saturated hydraulic conductivity. Vadose Zone Journal, 6:423-431.
- Amutha, R., and Porchelvan, P. 2011. Seasonal Prediction of Groudwater levels using ANFIS and Radial Basis Neural Network. International Journal of Geology, Earth and Environmental Sciences, 1: 98-108.
- Araghinejad, Sh. 2013. Data-driven modelling: using MATLAB in water resources and environmental engineering (Water Science and Technology Library), Springer, 400 p.
- Barzegar, A. 2001. Saline and Sodic Soils: Productivity and Efficiency. Shahid Chamran University Press, 273p (In Persian)
- Bhargava, G.P., and Abrol, I.P. 1978. Characteristics of some typical salt affected soils of Uttar Pradesh. Division of Soils and Agronomy, Central Soil Salinity Research Institute.
- Bouyoucos, G.J. 1962. Hydrometer method improved for making particle size analysis of soils. Agronomy Journal, 56: 464-466.
- Bower, C.A., Reitemeier, R.F., and Fireman, M. 1952. Exchangeable cation analysis of saline and alkali soils. Soil Science, 73: 251-261.
- Chapman, H.D. 1965. Cation exchange capacity. In Black, C.A (ed.), Methods of soil analysis. Part 2. ASA, Monograph, No. 9. Madison (WI): ASA.
- Dahiya I.S., Richter J., and Malik R.S. 1984. Soil spatial variability: A review. International Journal of Tropical Agriculture, 11:1-102.
- Dia X., Huo Z., and Wang H. 2011. Simulation for response of crop yield to soil moisture and salinity with artificial neural network. Field Crops Research, 121: 441-449.
- Erzin, Y., and Güneş, N. 2011. The prediction of swell percent and swell pressure by using neural networks. Mathematical and Computational Applications, 16: 425-436.
- Farahmand, A., Oustan, S.H., Jafarzadeh, A.J., and Asgarzad, A.N. 2011. The parameters of sodium and salinity in some salt affected soils of the Tabriz Plain. Journal of Soil and Water, 22: 1-15 (In Persian).
- Fireman, M., and Wadleigh, C.H. 1951. A statistical study of the relation between pH and the exchangeable-sodium-percentage of western soils. Soil Science, 71(4): 273-286.
- Food and Agriculture Organization of the United Nations (2008). Land Resources, Management, Planning and Use. http://www.fao.org/ag/agl/agll/spush (accessed April 2015).
- Haykin, S. 1994. Neural Networks: A Comprehensive Foundation. Macmillan, New York, 850 p.
- Jang, J.S.R. 1993. ANFIS-Adaptive-network-based fuzzy inference system. IEEE Transactions System Man Cybernetics, 23: 665-658.
- Jurinak, J.J., Amrhein, C., and Wagenet, R.J. 1984. Sodic hazard: The effect of SAR and salinity in soils and overburden materials. Soil Science, 137: 152 -158.
- Karami, A., and Afiuni-zadeh, S. 2012. Sizing of rock fragmentation modeling due to bench blasting using adaptive neuro-fuzzy inference system and radial basis function. International Journal of Mining Science and Technology, 22: 459–463.
- Kemp, S., Zaradic, P., and Hansen, F. 2007. An approach for determining relative input parameter importance and significance in artificial neural networks. Ecological Modelling, 204: 326-334.
- Keshavarzi, A., Sarmadian, F., Sadeghnejad, M., and Pezeshki, P. 2010. Developing pedotransfer functions for estimating some soil properties using artificial neural network and multivariate regression approaches. Proenvironment Promediu, 3: 322-330.
- Kumar M., Raghuwanshi N.S., Singh R., Wallender W.W., and Pruitt W.O. 2002. Estimating evapotranspiration using artificial neural network. Journal of Irrigation and Drainage Engineering-ASCE, 128: 224-233.
- Mashrei, M.A., Abdulrazzaq, N., Abdalla, T.Y., and Rahman, M.S., 2010. Neural networks model and adaptive neuro-fuzzy inference system for predicting the moment capacity of ferrocement members. Engineering Structures, 32: 1723-1734.
- Minasny, B., Hopman, J., Harter, W.T., Eching, S.O., Toli, A., and Denton, M.A. 2004. Neural networks prediction of soil hydraulic functions for alluvial soils using multistep outflow data. Soil Science Society of America Journal, 68: 417-429.
- Mohamadi, J., and Tahri, S.M. 2005. Fitting the pedotransfer functions by using the fuzzy regression. Science and Technology of Agriculture and Natural Resources, 2: 51-60 (In Persian).
- Nelson, R.E. 1982. Carbonate and gypsum. In Page, A.L. (ed.), Methods of soil analysis. Part 1. 2nd ed. ASA, Monograph, No. 9. Madison (WI): ASA.
- Noori, R., Hoshyaripour, G., Ashrafi, K., and Najdar Araabi, B. 2009. Uncertainty analysis of developed ANN and ANFIS models in prediction of carbon monoxide daily concentration. Atmospheric Environment, 44: 476-482.
- Olden, J.D., Joy, M.K., and Death, R.G. 2004. An accurate comparison of methods for quantifying variable importance in artificial neural networks using simulated data. Ecological Modelling, 178: 389-397.
- Olden, J.D., and Jackson, D.A. 2002. Illuminating the black box approach for understanding variable contributions in artificial neural networks randomization. Ecological Modelling, 154: 135-150.
- Rhoades, J.D. 1982. Cation exchange capacity. In Page, A.L. (ed.), Methods of soil analysis. Part 2. 2nd ed. ASA, Monograph, No. 9. Madison (WI): ASA.
- Rhoades, J.D. 1968. Mineral weathering correction for estimating the sodium hazard of irrigation waters. Soil Science Society of America Proceedings, 32: 648-652.
- Riahi Modvar, H.R., and Ayyoubzadeh, S.A. 2008. Estimating longitudinal dispersion coefficient of pollutants using adaptive neuro-fuzzy inference system. Journal of Water and Wastewater, 67: 34-47 (in Persian).
- Richards, L.A. 1954. Diagnosis and improvement of saline and alkali soils. In USDA Handbook 60. U.S. Department of Agriculture, Washington, DC.
- Ross, T.J. 1995. Fuzzy Logic with Engineering Application. McGraw Hill Inc. USA. 585 p.
- Rowell, D.l. 1994. Soil Science: Methods and Application. Longman Group, Harlow, England, 345p.
- Sadrmomtazi, A., Sobhani, J., and Mirgozar, M.A. 2013. Modeling compressive strength of EPS lightweight concrete using regression, neural network and ANFIS. Journal of Construction and Building Materials, 42: 205-216.
- Singh, P. and Deo, M.C. 2007. Suitability of different neural networks in daily flow forecasting. Applied Soft Computing, 7: 968-978.
- USDA-NRCS. 1996. Soil Survey Laboratory Methods Manual. Soil Survey Investigations. Report, No. 42.Version 3.0. Nebraska.
- Walkley, A., and Black, I.A. 1934. An examination of the Degtjareff method for determining soil organic matter, and proposed modification of the chromic acid titration method. Soil Science, 37: 29-38.
- Wang, Y.G., Xiao, D.N., Li,Y., and Li, X.Y. 2008. Soil salinity evolution and its relationship with dynamics of groundwater in the oasis of inland river basins: case study from the Fubei region of Xinjiang province. China Environmental Monitoring and Assessment, 140: 291-302.
- Wiegand, C.L., Lyles, L., and Carter, D.L. 1966. Interspersed salt-affected and unaffected dryland soils of the lower Rio Grande Valley: II. Occurrence of salinity in relation to infiltration rates and profile characteristics. Soil Science Society of America Journal, 30(1): 106-110.
- Yilmaz, I., and Kaynar, O. 2011. Multiple regression, ANN (RBF, MLP) and ANFIS models for prediction of swell potential of clayey soils. Expert Systems with Applications, 38: 5958-5966.