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کاربرد شبکه عصبی مصنوعی در پیش‌بینی هدایت هیدرولیکی اشباع با استفاده از پارامترهای فیزیکی خاک | ||
| تحقیقات مهندسی صنایع غذایی | ||
| مقاله 7، دوره 10، شماره 1، خرداد 1388، صفحه 97-112 اصل مقاله (512 K) | ||
| نوع مقاله: مقاله پژوهشی | ||
| نویسندگان | ||
| بهزاد قنبریان علویجه* 1؛ عبدالمجید لیاقت2؛ سمانه سهرابی3 | ||
| 1دانشجوی دکتری دانشکده آب و خاک پردیس کشاورزی و منابع طبیعی دانشگاه تهران | ||
| 2دانشیار گروه آبیاری و آبادانی دانشکده آب و خاک پردیس کشاورزی و منابع طبیعی دانشگاه تهران | ||
| 3دانشجوی کارشناسی ارشد گروه مهندسی آب دانشکده کشاورزی دانشگاه تبریز | ||
| چکیده | ||
| ویژگیهای هیدرولیکی خاک همچون هدایت هیدرولیکی اشباع و غیراشباع در مطالعات زیست محیطی نقش مهمی را ایفا مینمایند. از آنجائیکه اندازهگیری مستقیم این قبیل ویژگیهای هیدرولیکی خاک امری وقتگیر و هزینهبر است روشهای غیرمستقیمی چون توابع انتقالی و شبکههای عصبی مصنوعی بر مبنای پارامترهای سهل الوصول خاک توسعه یافتهاند. در این خصوص در این مطالعه، از شبکه عصبی مصنوعی به منظور تخمین هدایت هیدرولیکی اشباع خاک با استفاده از دادههای اندازهگیری شده منحنی مشخصه رطوبتی خاک و جرم مخصوص ظاهری استفاده شده است. با استفاده از دادههای اندازهگیری شده جرم مخصوص ظاهری خاک، بعد فرکتالی منحنی مشخصة رطوبتی، مکش در نقطه ورود هوا، تخلخل مؤثر، مقادیر هدایت هیدرولیکی اشباع خاک با استفاده از شبکه عصبی مصنوعی تخمین زده شدند. در مرحله آموزش مدل از 114 داده اندازهگیری شده منحنی مشخصة رطوبتی و جرم مخصوص ظاهری خاک و در مرحله آزمون از 28 داده باقیمانده استفاده شد. مقادیر MSE و R2 در مرحله آزمون مدل شبکه عصبی مصنوعی با چهار پارامتر ورودی بهترتیب 0028/0 و 76/0 محاسبه شدند. مقایسه عملکرد مدل شبکه عصبی مصنوعی با دو مدل ارائه شده توسط رائولز و همکاران نشان داد که مدل شبکه عصبی مصنوعی با دقت بالاتری هدایت هیدرولیکی اشباع خاک را پیش‌بینی مینماید. | ||
| عنوان مقاله [English] | ||
| Application of Artificial Neural Networks in Prediction of Saturated Hydraulic Conductivity Using Soil Physical Parameters | ||
| چکیده [English] | ||
| Soil hydraulic properties such as saturated and unsaturated hydraulic conductivity play an important role in environmental research. Since direct measurement of these soil hydraulic properties is time-consuming and costly, indirect methods such as pedotransfer functions and artificial neural networks (ANN) were developed based on readily available parameters. In this study, the use of ANN to predict saturated hydraulic conductivity using a measured soil moisture curve and bulk density was investigated. Measured bulk density and soil moisture curves were used to estimate saturated hydraulic conductivity from calculated fractal dimensions, air entry values, bulk density and effective porosity using ANN. In the training and testing steps of ANN, 114 and 28 measured soil samples were used, respectively. R2 and MSE were 0.76 and 0.0028, respectively, for the ANN method with four inputs. A comparison of ANN with Rawls et al. (1993, 1998) models showed that the neural network more accurately predicts saturated hydraulic conductivity. | ||
| کلیدواژه‌ها [English] | ||
| Artificial Neural Network, Saturated hydraulic conductivity, soil moisture curve | ||
| مراجع | ||
|
Ahuja, L. R., Naney, J. W., Green, R. E. and Nielsen, D. R. 1984. Macroporosity to characterize spatial variability of hydraulic conductivity and effects of land management. Soil Sci. Soc. Am. J. 48, 699-702.
Ahuja, L. R., Cassel, D. K., Bruce, R. R. and Barnes, B. B. 1989. Evaluation of spatial distribution of hydraulic conductivity using effective porosity data. Soil Sci. 148, 404-411.
Alizadeh, A. 2004. Soil Physics. Imam Reza University Pub. (in Farsi)
Alizadeh, A. 2005. Soil, Water, Plant Relationships. Imam Reza University Pub. (in Farsi)
Amini, M., Abbaspour, K. C., Khademi, H., Fathianpour, N., Afyuni, M. and Schulin, R. 2005. Neural network models to predict cation exchange capacity in arid regions of Iran. European J. Soil Sci. 56, 551-559.
Anon. 2006. MATLAB: The Language of Technical Computing. version 7.3.
Bouma, J. 1989. Using soil survey data for quantitative land evaluation. Adv. Soil Sci. 9, 177-213.
Brakensiek, D. L., Rawls, W. J. and Stephenson, G. R. 1984. Modifying SCS hydrologic soil groups and curve numbers for rangeland soils. ASAE Paper PNR-84-203. St. Joseph. MI 49085-9659. USA.
Campbell, G. S. 1985. Soil Physics with Basic. Elsevier. New York.
Carman, P. C. 1938. The determination of the specific surface of powders. J. Soc. Chem. Ind. Trans. 57, 225.
Carman, P. C. 1956. Flow of gases through porous media. Butterworths. Scientific Pub. London.
Carrier, W. D. 2003. Goodbye, Hazen; Hello, Kozeny-Carman. J. Geotech. Geoenviron. Eng. 129, 1054-1056.
Cosby B. J., Hornberger, G. M., Clapp, R. B. and Ginn, T. R. 1984. A statistical exploration of soil moisture characteristics to the physical properties of soils. Water Resour. Res. 20, 682-690.
Dane, J. H. and Puckett, W. 1994. Field soil hydraulic properties based on physical and mineralogical information. In: van Genuchten, M. Th. (Eds) Proceedings of the International Workshop on Indirect Method for Estimation Hydraulic Properties of Unsaturated Soils. California University. Riverside. CA. 389-403.
Dexter, A. R. 2004. Soil physical quality Part I. Theory, effects of soil texture, density, and organic matter, and effects on root growth. Geoderma 120, 201-214.
Doaee, M., Shabanpour sharestani, M. and Bagheri, F. 2005. Modeling saturated hydraulic conductivity in clay soils in guilan province (Iran) using artificial neural networks. J. Agric. Sci. 1: 41-48. (in Farsi)
Franzmeier, D. P. 1991. Estimation of hydraulic conductivity from effective porosity data for some Indiana soils. Soil Sci. Soc. Am. J. 55: 1801-1803.
Ghanbarian-Alavijeh, B., Lighat, A. M., Shorafa, M. and Moghimi-Araghi, S. 2008. Prediction of soil water retention curve using soil particle-size distribution. J. Agric. Eng. Res. 9, 63-80. (in Farsi)
Haverkamp, R., Zammit, C., Boubkraoui, F., Rajkai, K., Arrúe, J. L. and Heckmann, N. 1997. GRIZZLY. Grenoble soil catalogue: Soil survey of field data and description of particle-size, soil water retention and hydraulic conductivity functions. LTHE. Grenoble. France.
Hazen, A. 1911. Discussion of Dams on sand foundations by A. C. Koenig. Trans. Am. Soc. Civ. Eng. 73, 199-203.
Huang, G. and Zhang, R. 2005. Evaluation of soil water retention curve with the pore-solid fractal model. Geoderma. 127, 52-61.
Jabro, J. D. 1992. Estimation of saturated hydraulic conductivity of soils from particle size distribution and bulk density data. Trans. ASAE. 35, 557-560.
Jain, A. and Kumar, A. 2006. An evaluation of artificial neural network technique for the determination of infiltration model parameters. Appl. Soft Comput. 6, 272-282.
Karamooz, M. and Araghinejad, S. 2005. Advance Hydrology. Amir Kabir University Pub. (in Farsi).
Kozeny, J. 1927. Ueber kapillare Leitung des Wassers im Boden. Wien. Akad.
Leij, F. J., Alves, W. J., van Genuchten, M. Th. and Williams, J. R. 1996. Unsaturated soil hydraulic database. UNSODA 1.0 user’s manual. Rep. EPA/600/R96/095. USEPA.
Marshall, T. J. 1958. A relation between permeability and size distribution of pores. European J. Soil Sci. 9, 1-8.
Menhaj, M. 2000. Fundamental of artificial neural network. Amir kabir Pub. (in Farsi)
Merdun, H., Ozer, C., Meral, R. and Apan, M. 2006. Comparison of artificial neural network and regression pedotransfer functions for prediction of soil water retention and saturated hydraulic conductivity. Soil & Till. Res. 90, 108-116.
Messing, I. 1989. Estimation of saturated hydraulic conductivity in clay soils from soil moisture retention data. Soil Sci. Soc. Am. J. 53, 665-668.
Minasny, B. and McBratney, A. B. 2002. Neural Networks Package for fitting pedotransfer function. Technical Notes. Version 1.0.
Navabian, M., Liaghat, A. M. and Homaee, M. 2004. Estimating soil saturated hydraulic conductivity using pedotransfer functions. J. Agric. Eng. Res. 4, 1-11. (in Farsi)
Pachepsky, Y. A., Timlin, D. J. and Ahuja, L. R. 1999. Estimating saturated soil hydraulic conductivity using water retention data and neural networks. Soil Sci. 164, 552-560.
Parasuraman, K., Elshorbagy, A. and Si, B. C. 2006. Estimating saturated hydraulic conductivity in spatially variable fields using neural network in Ensembles. Soil Sci. Soc. Am. J. 70, 1851-1859.
Puckett, W. E., Dane, J. H. and Hajek, B. F. 1985. Physical and mineralogical data to determine soil hydraulic properties. Soil Sci. Soc. Am. J. 49, 831-836.
Rawls, W. J., Brakensiek, D. L. and Saxton, K. E. 1982. Estimation of soil water properties. Trans. ASAE. 25, 1316-1320.
Rawls, W. J., Brakensiek, D. L. and Logsdon, S. D. 1993. Predicting saturated hydraulic conductivity utilizing fractal principles. Soil Sci. Soc. Am. J. 57, 1193-1197.
Rawls, W. J., Gimenez, D. and Grossman, R. 1998. Use of soil texture, bulk density, and slope of the water retention curve to predict saturated hydraulic conductivity. Trans. ASAE. 41, 983-988.
Saxton, K. E., Rawls, W. J., Romberger, J. S. and Papendick, R. I. 1986. Estimating generalized soil water characteristics from texture. Soil Sci. Soc. Am. J. 50, 1031-1036.
Schaap, M. G., Leij, F. J. and van Genuchten, M. Th.2001. Rosetta: A computer program for estimating soil hydraulic parameters with hierarchical pedotransfer functions, J. Hydrol. 251, 163-176.
Spychalski, M, Kaźmierowski, C. and Kaczmarek, Z. 2007. Estimation of saturated hydraulic conductivity on the basis of drainage porosity. EJPAU 10(1): 04 (http://www.ejpau.media.pl/ volume10/issue1/art-04.html).
Timlin, D. J., Ahuja, L. R. and Williams, R. D. 1996. Methods to estimate soil hydraulic parameters for regional scale applications of mechanistic methods. In Application of GIS to the Modeling of Non-point Source Pollutants in the Vadose Zone. SSSA Agronomy. Madison. WI. 185-203.
Tyler, S. W. and Wheatcraft, S. W. 1990. Fractal processes in soil water retention. Water Resour. Res. 26, 1047-1054.
Vereecken, H., Maes, J., Feyen, J. 1990. Estimating unsaturated hydraulic conductivity from easily measured soil properties. Soil Sci. 149, 1-12.
Wösten, J. H. M. 1997. Pedotransfer functions to evaluate soil quality. In: Gregorich, E. G. and Carter, M. R. (Eds) Soil Quality for Crop Production and Ecosystem Health. Developments in Soils Science. Elsevier. Amsterdam. 25, 221-245.
Wösten, J. H. M., Lilly, A., Nemes, A. and Le Bas, C. 1999. Development and use of a database of hydraulic properties of European soils. Geoderma. 90, 169-185. | ||
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