1- Argyrokastritis I., and Kerkides P. 2003. A note to the variable sorptivity infiltration equation. Water Resour Manage, 17: 133-145.
2- ASTM. 2003. D3385-03 Standard test method for infiltration rate of soils in field using double-ring infiltrometer. 2-Annual Book of ASTM Standards 04,08. American Society for Testing and Materials, West Conshohocken, PA.
3- Box G.E.P. , and Jenkins G.M. 1976. Time Series Analysis: Forecasting and Control. Holden-Day, San Francisco.
4- Dickey D.A., and Fuller W.A. 1979. Distribution of the Estimators for Autoregressive Time Series with a Unit Root. the American Statistical Association, 74 (366): 427–431.
5- Gujarati D. N. 1999. Basic econometrics. New York Graw Hill International Edition, 838.
6- Horton R.E. 1940. Approach towards a physical interpretation of infiltration capacity. Soil Science. Society of . America Proceedings, 5: 399-417.
7- Haykin S. 1994. Neural Networks: a Comprehensive Foundation. Macmillan. New York, 340.
8- Kostiakov A.N. 1932. On the dynamics of the coefficient of water percolation in soils and the necessity for studying it from a dynamic point o view for purpose of amelioration. Trans. Int. Congr. Soil Science, (A): 17-21.
9- Loaiciga H.A., and Huang A. 2007. Ponding analysis with Green-Ampt infiltration. Hydrologic Engineering, 12(1):109-112.
10- Machiwal D., Jha M.K., and Mal B.C. 2006. Modelling Infiltration and quantifying Spatial Soil Variability in a Wasteland of Kharagpur, India. Biosystems Engineering, 95(4): 569-582.
11- Mohammadi M.H., and Refahi. H. 2005. Estimating parameters of infiltration equations using soil physical properties. Agricultural Science Iran, 36(6): 1391-1398. (in Persian with English abstract)
12- Nahvinia M.J., Liaghat A., Parsinejad. M. 2010. Prediction of Depth of Infiltration in Furrow Irrigation Using Tentative and Statistical Models. Water and Soil, 24 (4): 769-780. (in Persian with English abstract)
13- Nasseri A., Neyshabori M.R., and Fakheri fard A. 2013. Time series analysis of furrow infiltration. Irrigation. and Drainage, 62: 640-648.
14- Niromand H.A., and Bozorgnia, A. (translators), 1993. Introduction for Time Series Analysis, C. Chetfil, Published by Mashhad Ferdowsi University, 290 pp. 16.
15- Philips P.C.B., Perron P. 1988. Testing for unit root in time series regression. Journal of Biometrika, 75: 335-346.
16- Pulido-Calvo I., Rolda´n J., Lo´pez-Luque R., Gutie´rrez-Estrada J.C. 2003. Demand Forecasting for Irrigation Water Distribution Systems. Irrigation and Drainage Engineering, 129(6): 422-431.
17- Sadorsky P. 2006. Modeling and forecasting petroleum futures volatility, Energy Economics, 28: 467-488.
18- Schwankl L., Raghuwanshi N., Wallender W. 2000. Time series modeling for prediction spatially variable infiltration. Irrigation and Drainage Engineering, 126: 283-287.
19- Sy N.L. 2006. Modelling the infiltration process with a multi-layer perceptron artificial neural network. Hydrological Sciences, 51(1): 3-20.
20- Tisu P., Guitjens J., 1986. Predicting EC for drainage water management. Irrigation and Drainage Engineering, 112: 274-281.
21- Turner E.R. 2006. Comparison of infiltration equations and their field validation with rainfall simulation. M.Sc. Thesis, University of Maryland, USA, 202.
22- Unkown. 2001. Instructions of soil infiltration rate measurement using double ring. Iran Planning and Budget Organization, Publication No. 243. (in Persian)
23- Unkown. 2003. Infiltration report in Lali plain. Soil and Water Consulting Engineers. No. 25-6. (in Persian)
24- Walker W. R. 1998. SIRMOD – Surface Irrigation Modeling Software. Utah State University.
25- Weiler M. 2005. An infiltration model based on flow variability in macropores: development, sensitivity analysis and applications. Hydrology. 310: 294-315.
26- Zoua P., Yanga J., Fub J., Liu G., Li D. 2010. Artificial neural network and time series models for predicting soil salt and water content. Agricultural Water Management, 97: 2009– 2019.