Department of Watershed Management Engineering, Faculty of Agriculture, Lorestan University, Khorramabad, Iran.
Determination of the infiltration rate in a watershed is not easy and in empirical and theoretical point of view, it is important to access average value of infiltration. Infiltration models has main role in managing water sources. Therefore different types of models with various degrees of complexity were developed to reach this aim. Most of the estimating methods of soil infiltration are expensive and time consuming and these methods estimate infiltration with hypothesis of zero slope. One of the conceptual and physical models for estimating soil infiltration is Green-Ampt model which is similar to Richard model. This model uses slope factor in estimating infiltration and this is the power point of Green-Ampt model. In this research the empirical model of Green-Ampt was optimized with integrating artificial neural network model (ANN) and a model of geographical information system WMS to estimate the infiltration in Kakasharaf watershed. Results of the comparison between the output of this method and real value of infiltration in region (through multiple cylinders) showed that this method can estimate the infiltration rate of Kakasharaf watershed with low error and acceptable accuracy (Nash-Sutcliff performance coefficient 0.821, square error 0.216, correlation coefficient 0.905 and model error 0.024).
Haghizadeh, A. , Soleimani, L. and Zeinivand, H. (2014) Optimization of the Conceptual Model of Green-Ampt Using Artificial Neural Network Model (ANN) and WMS to Estimate Infiltration Rate of Soil (Case Study: Kakasharaf Watershed, Khorram Abad, Iran). Journal of Water Resource and Protection, 6, 473-480. doi: 10.4236/jwarp.2014.65047.
[1] Skandarinia, A., ZiatabarAhmadi, M., Nazarpour, H., Teimori, M. and Moshfegh, M.Z. (2005) Investigation of the Effects of Previous Rainfall Factor in Estimating of Stream Flow Using the Modeling of Rainfall-Runoff. Eight International Congress of Civil Engineering, Shiraz University, 12 May 2005. [2] El-shafie, A., Mukhlisin, M., Najah, A.A. and Taha, M.R. (2011) Performance of Artificial Neural Network and Regression Techniques for Rainfall-Runoff Prediction. International Journal of Physical Sciences, 6, 1997-2003. [3] Braud, I., De Condappa, D. and Soria, J.M., Haverkamp, R., Angulo-Jaramillo, R., Galle, S. and Vauclin, M. (2005) Use of Scaled Forms of the Infiltration Equation for the Estimation of Unsaturated Soil Hydraulic Properties (the Beerkan Method). European Journal of Soil Science, 56, 361-374. http://dx.doi.org/10.1111/j.1365-2389.2004.00660.x [4] Mohammadi, M.H. and Refahi, G.H. (2005) Estimation of the Equation Parameters of Infiltration Using Physical Properties of Soil. Journal of Agricultural Science, 36, 1398-1391. [5] Fernández-Gálvez, J., Barahona, E. and Mingorance, M. (2008) Measurement of Infiltration in Small Field Plots by a Portable Rainfall Simulator Application to Trace-Element Mobility. Water, Air, Soil Pollute, 191, 257-264.
http://dx.doi.org/10.1007/s11270-008-9622-2 [6] Vaghefi, M. (2004) Definition of the Conceptual Infiltration Model and Converting Crude Rainfall to Pure Rainfall in Southern Watersheds of Country. First National Conference of Civil Engineering, 22-23 May 2004. [7] Chu, S.T. (1978) Infiltration during Unsteady Rain. Water Resources Research, 14, 461-466.
http://dx.doi.org/10.1029/WR014i003p00461 [8] Chu, S.T. (1985) Modeling Infiltration on Tilled Soil during Non-Uniform Rain. Transaction of the ASAE 28, 4, 1226-1232. http://dx.doi.org/10.1029/WR014i003p00461 [9] Hsu, S.M., NI, C.F. and Hung, P.F. (2002) Assessment of Three Infiltration Formulas Based on Model Fitting and Richards Equation. Journal of Hydrology, 7, 373-379. http://dx.doi.org/10.1061/(ASCE)1084-0699(2002)7:5(373) [10] Ma, Y., Feng, S., Su, D., Gao, G. and Huo, Z. (2009) Modeling Water Infiltration in a Large Layered Soil Column with a Modified Green-Ampt Model and HYDRUS-1 D. Computers in Agriculture. [11] Dorofki, M., Elshafie, A.H., Jaafar, O., Karim, O.A. and Abdullah, S.M.S. (2011) A GIS-Based Survey for the Optimization of Infiltration Forecasting Models with Emphasis on Slope Effect and Land Use. International Journal of the Physical Sciences, 6, 5738-5751. [12] Green, W.H. and Ampt, C.A. (1911) Studies on Soil Physics, I. Flow of Water and Air through Soils. Journal of Agricultural Science, 4, 1-24. [13] Brakensiek, D.L. and Onstad, C.A. (1977) Parameter Estimation of the Green and Ampt Infiltration Equation. Water Resources Research, 13, 1009-1012. http://dx.doi.org/10.1029/WR013i006p01009 [14] Smith, J. and Eli, R.B. (1955) Neural Network Models of Rainfall-Runoff Process. Journal of Water Resources Planning and Management, 4, 232-239. [15] Sabol, G., Rumann, J., Khalili, D., Waters, S. and Lehman, T. (1995) Rainfall Losses in Drainage Design Manual for Maricopa County, Arizona, Vol. 1: Hydrology. Flood Control District of Maricopa County, Arizona, 1-19. [16] Akbarpour, M., Rahnama, B. and Barani, G.H. (2003) Comparison of the Artificial Neural Network and HEC-HMS Model in Rainfall Process. 4th Hydraulic Conferences of Iran, Shiraz, September 2003. [17] Tokar, A.S. and Markus, M. (2000) Precipitation-Runoff Modeling Using Artificial Neural Networks and Conceptual Models. Journal of Hydrologic Engineering, 5, 156-161.
http://dx.doi.org/10.1061/(ASCE)1084-0699(2000)5:2(156) [18] Smemoe, C.M., Nelson, E.J. and Zhao, B. (2004) Spatial Averaging of Land Use and Soil Properties to Develop the Physically-Based Green and Ampt Parameters for HEC-1. Environmental Modelling & Software, 19, 525-535.
http://dx.doi.org/10.1016/j.envsoft.2003.07.001 [19] Mousavi, S.A., Asadi, H., Norouzi, M. and Ghavidel far, S. (2010) Investigation of the Green-Ampt Model in Navroud Indicator Watershed Using Field Survey and Infiltration Measurement with Double Rings. Research Findings of Water Company of Guilan Province.