Ahmed, S., & Simonovic, S.P. (2005). An Artificial Neural Network model for generating hydrograph from hydro meteorological parameters .Journal of Hydrology, 315, 236-251.
Alemi, M.H., Azari, A.S., & Nielsen, D.R. (1980). Krigingand univariate modeling of a spatial correlateddata. Soil Technology, 1, 133-147.
Azizpour, S., Fathi, P., & Nobakhtvakili, K. (2012). Estimating saturated hydraulic conductivity and porosity coupled with intelligent inverse problem approach. Journal of Science and Technology of Agriculture and Natural Resources, Soil and Water Sciences, 60, 13-22. (In Persian).
Basaran, M., Erpul, G., Ozcan, A.U., Saigon, D.S., Kibar, M., Bayramin, I., & Yilman, F.E. (2011). Spatial information of soil hydraulic conductivity and performance of cokriging over kriging in a semi-arid basin scale. Environmental Earth Sciences, 63, 827–838.
BegayHerchgan, H., Heshmati, S.S. (2012). In dicators of ground water quality zoning of Shahrekord’s for using on irrigation system design. Agriculture Water Reourcess., 51, 24-61. (In Persian).
Cadenas, E., & Rivera, W. (2009). Short term wind speed forecasting in La Venta, Oaxaca, Me´xico, using Artificial Neural Networks. Renewable Energy, 34, 274–278.
Dahamsheh, A., & Aksoy, H. (2009). Artificial Neural Network models for forecasting inter mittent monthly precipitation in arid regions. Meteorological Applications, 16, 325-337.
Delbari, M., Tailor mood, M., & Mahdian, M.H. (2004). Evaluation of methods for estimating the hydraulic conductivity of the soil in areas of steep terrain and the bottom surface water of the Sistan plain water. Journal of Agricultural Science,5, 1-12. (In Persian).
Doaie, M., Shabanpour, M., & Bagheri, F. (2005). Modeling of saturated hydraulic conductivity of Gilan Province involving Artificial Neural Networks. The agricultural Science Research Report. Faculty of Agriculture, GilanUnivesity. (In Persian).
Golshadfasih, M., Hooshmand, A.A., & Mehdinezhadiani, B. (2014). Application of genetic algorithms for estimating the saturated soil hydraulic parameters. Journal of Soil Research (Soil and Water Sciences). 28(1), 144-151. (In Persian).
Haghverdi, A., Ghahreman, B., Jalini, M., Khoshnudyazdi, A.A., & Arabi, Z. (2011). Comparison of different methods of artificial intelligence modeling of soil moisture characteristic curve in North and North East of Iran. Research Journal of soil and water conservation methods, 18(2), 65-84. (In Persian).
Hoseini, Y. (2004). Drainag parameters optimization using geostatistical methods (Cokriging) and their impact ondrain spacing. Faculty of Water Science Engineering, Chamran University. (In Persian).
Hosseini, E., Gallich and, J, & Caren, J. (1993). Comparison of several in terpol ators for smoothing hydrauliccond uctivity data in south west Iran. Transactions of the ASAE, 36,1687-1693.
Kaplana, H.K., & Aggrawal, P. (2011). Geostatistical Analyst for Deciding Optimal Interpolation Strategies for Delineating Compact Zones. International Journal of Geosciences, 2, 585-596.
Kayadelen, C. (2011). Soil liquefaction modeling by Genetic Expression Programming and Neuro-Fuzzy. Expert Systems with Applications, 38, 4080-4087.
Kim, S., & Kim, H. (2008). Neural Networks and genetic algorithm approach for nonlinear evaporation and evapotrans piration modeling. Hydrology, 351, 299-317.
Merdun, H., Ozer, C., Meral, R., & 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.
Navabian, M., Liaghat, A.M., & Homaee,M.(2004). Estimating soil saturated hydraulic conductivity using pedotransfer functions. Journal of Agricultural Engineering Research, 4,1-11.
Nezami, M.T., & Alipour, Z.T. (2012). Preparing of the soil salinity map using geastatistics method in Qazvin plaine. Journal of Soil Science and Environmental Management, 3, 36-41.
Prasad, R., & Mathur, S. (2007). Ground water Flow and Contaminant Transport Simulation with Imprecise Parameters. Irrigation and Drainage Engineering, 133(1) 61-70.
Salazar, O., Wesstrom, I., & Joel. A. (2008). Evaluation of Drainmod using saturated hydraulic conductivity stimated by a pedestrians fer function model. Journal of Agricultural Water Management, 95, 1135 – 1143.
Venkatesan, P., & Anitha,S. (2006). Application of a radial basis function neural network for diagnosis of diabetes mellitus. Current Science, 91, 1195–1199.
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