| تعداد نشریات | 24 |
| تعداد شمارهها | 887 |
| تعداد مقالات | 7,912 |
| تعداد مشاهده مقاله | 14,909,871 |
| تعداد دریافت فایل اصل مقاله | 12,670,469 |
An evaluation of genetic algorithm method compared to geostatistical and neural network methods to estimate saturated soil hydraulic conductivity using soil texture | ||
| Iran Agricultural Research | ||
| مقاله 11، دوره 36، شماره 1، تیر 2017، صفحه 91-104 اصل مقاله (1.26 M) | ||
| نوع مقاله: Full Article | ||
| شناسه دیجیتال (DOI): 10.22099/iar.2017.4039 | ||
| نویسندگان | ||
| Y. Hosseini* 1؛ R. Sedghi2؛ S. Bairami2 | ||
| 1Moghan College of Agriculture and Natural Resources, University of Mohaghegh Ardabili, Ardabil, I. R. Iran | ||
| 2Sama Technical and Vocatinal Training College, Islamic Azad University, Ardabil Branch, Ardabil, I. R. Iran | ||
| چکیده | ||
| ABSTRACT-Determining hydraulic conductivity of soil is difficult, expensive, and time-consuming. In this study, Algorithm Genetic and geostatistical analysis and Neural Networks method are used to estimate soil saturated hydraulic conductivity using the properties of particle size distribution. The data were gathered from 134soil profiles from soil and lander form studies of the Ardabil Agricultural Organization. Results showed that Or denary cokriging has the best fit for the geostatistical methods. The best-fitted vario gram was the exponential model with anugget effect of 0 cm day-1 and sill of 156 cm day-1 which is the strength of the spatial structure and full effect of the structural components on the vario gram model for the region; also, in the or denary cokriging method, an accurate estimate was obtained using R2 = 0.93 and RMSE = 3.21.Multilayer perceptron (MLP) network used the Levenberg- Marquardt (trainlm) algorithm with are gression coefficient (R2) of 0.997 and Root Mean Square Error (RMSE) of 1.22 to estimate the hydraulic conductivity of saturated soil. For GA model, parameters of root mean square error (RMSE) cm day-1 and the coefficient of determination (R2) were determined as 1.35 and 0.926, respectively. Performance evaluation of the models showed that the Neural Networks model compared with geostatistical analysis and genetic algorithm was able to predict soil hydraulic conductivity with high and more accuracy and results of this method was closer to the measurement results. | ||
| کلیدواژهها | ||
| Keywords:؛ Geostatistics؛ Saturated hydraulic conductivity Neural Network Methods (ANN) Cokriging؛ Genetic Algorithm | ||
| مراجع | ||
|
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. | ||
|
آمار تعداد مشاهده مقاله: 1,700 تعداد دریافت فایل اصل مقاله: 1,953 |
||