شبکه عصبی مصنوعی هدایت هیدرولیکی معادله فلیپ ضریب جذب نفوذ آب

نوع مقاله : مقاله کامل

نویسندگان

بخش مهندسی آب ، دانشکده کشاورزی، دانشگاه شیراز، شیراز، ج. ا. ایران

چکیده

نفوذپذیری آب در خاک یکی از مهم‌ترین پدیده‌های فیزیکی خاک است. روش‌های تجربی تعیین معادله‌های نفوذ، نیازمند انجام آزمایش‌های زمان بر و پرهزینه است، لذا در این پژوهش از روش غیرمستقیم شبکه عصبی مصنوعی برای تخمین مقادیر ضریب جذب (S)  و فاکتور انتقال(A)  معادله فیلیپ استفاده شد. ساختارهای مختلف شبکه عصبی مصنوعی متشکل از الگوریتم های آموزش TrainLM  و TrainBR و توابع انتقال لوگ سیگموئید و تانژانت سیگموئید برای لایه‌های میانی و تابع تبدیل خطی برای لایه خروجی و ترکیبات متفاوتی از ورودی‌ها، شامل مقادیر نفوذ تجمعی و زمان‌های مربوط به هرکدام، به‌عنوان ورودی ثابت و درصد شن، درصد سیلت، درصد رس، چگالی ظاهری و ماده آلی به عنوان ورودی‌های متغیر، برای 30 نقطه در دانشکده کشاورزی واقع در منطقه باجگاه بررسی گردید. برای تخمین ضریب جذب بهترین ساختار دارای دو لایه مخفی و 3 ورودی (درصد شن، درصد سیلت و درصد رس) با دو نرون در لایه اول و سه نرون در لایه دوم و الگوریتم آموزش TrainLM بود. برای تخمین فاکتور انتقال بهترین ساختار دارای دو لایه مخفی و 5 ورودی (چگالی ظاهری، مقدار ماده آلی، درصد شن، درصد سیلت و درصد رس) با دو نرون در لایه اول و سه نرون در لایه دوم و الگوریتم آموزش Train BR بود. افزایش تعداد لایه‌های مخفی و تعداد ورودی‌ها تاثیر به سزایی در بهبود نتیجه داشت و شبکه عصبی در تخمین مقادیر فاکتور انتقال عملکرد بسیار بهتری نسبت به ضریب جذب را نشان داد. مقدار ضریب تعیین (R2) نشان داد که پیشبینی های شبکه عصبی برای A (% 6/84) بهتر از S (% 5/77) می‌‌باشد.

کلیدواژه‌ها


Basheer, I. A., & Hajmeer, M. (2000). Artificial neural networks: Fundamentals, computing, design, and application. Journal of Microbiological Methods, 43(1), 3-31. doi: http://dx.doi.org/10.1016/S0167-7012(00)00201-3
Blake, G., Hartge, K. p., & methods, m. (1986). Bulk density. In A. Klute(Ed), Methods of Soil Analysis: Part 1—Physical and Mineralogical Methods (pp. 363-375): Soil
Science Society of America, American Society of Agronomy.
Brown, M., & Chris, H. (1994). Neurofuzzy adaptive modeling and control. New York: Prentice Hall.
Cosby, B., Hornberger, G., Clapp, R., & Ginn, T. (1984). A statistical exploration of the relationships of soil moisture characteristics to the physical properties of soils.  Water Resources Research, 20(6), 682-690.
Gardner, W. R. (1958). Some steady-state solutions of the unsaturated moisture flow equation with application to evaporation from a water table. Soil Science, 85(4), 228-232.
Gee, G., & Bauder, G. (1986). Particle-size analysis. In A. Klute (Ed.), Methods of Soil Analysis: Part 1—Physical and Mineralogical Methods (pp. 383-409). American Society of Agronomy - Soil Science Society of America.
Ghobadian, B., Rahimi, H., Nikbakht, A., Najafi, G., & Yusaf, T. (2009). Diesel engine performance and exhaust  emission analysis using waste cooking biodiesel fuel with an artificial neural network. Renewable Energy, 34(4), 976-982.
Graupe, D. (2013). Principles of Artificial Neural Networks (Vol. 7). Chicago: World Scientific Publishing Co Pte Ltd.
Green, H. W., & Ampt, G. A. (1911). Studies on soil phyics. The Journal of Agricultural Science, 4(01), 1-24. doi: doi:10.1017/S0021859600001441
Hagan, M., Demuth, H., & Beale, M. (1996). Neural network design. Boston, MA, USA:  PWS Publishing Company.
Hillel, D., & Gardner, W. (1970). Transient infiltration into crust-topped profiles. Soil Science, 109(2), 69-76.
Holtan, H. N. (1961). A Concept for infiltration estimates in watershed engineering. Washington DC, USA:  Agricultural Research Service - U. S. Department of Agriculture.
Horton, R. E. (1940). Approach toward a physical interpretation of infiltration capacity. Soil Science Society of America Journal, 5, 339-417.
Ibn Ibrahimy, M., Ahsan, R., & Khalifa, O. O. (2013). Design and optimization of Levenberg-Marquardt based neural network classifier for EMG signals to identify hand motions. Measurement Science Review, 13(3), 142-151.
Igbadun, H., Othman, M., & Ajayi, A. (2016). Performance of selected water infiltration models in sandy clay loam soil in Samaru Zaria. Global Journal of Researches in Engineering: J General Engineering, 16, 8-14.
Jain, S., Singh, V., & van Genuchten, M. (2004). Analysis of soil water retention data using artificial neural networks. Journal of Hydrologic Engineering, 9(5), 415-420. doi: doi:10.1061/(ASCE)1084-0699(2004)9:5(415)
Kim, S. (2017). MATLAB Deep Learning With Machine Learning, Neural Networks and Artificial Intelligence. New York: Apress.
Kostiakov, A. V. (1932). On the dynamics of the coefficient of water percolation in soils and on the necessity for studying it from a dynamics point of view for purposes ofamelioration. Transactions of 6th Committee International Society of Soil Science, Russia, Part A, 17-21.
Kumar, M., Raghuwanshi, N., Singh, R., Wallender, W., & WO, P. (2002). Estimating evapotranspiration using artificial neural network. Journal of Irrigation and Drainage Engineering, 128(4), 224-233. doi: doi:10.1061/(ASCE)0733-9437(2002)128:4(224)
Lei, Z. D., Yang, S. X., & Xie, S. C. (1989). One step method of scaling the soil hydraulic properties in the field. Journal of Hydraulic Engineering, 12, 1-10.
Lili, M., Bralts, V. F., Yinghua, P., Han, L., & Tingwu, L. (2008). Methods for measuring soil infiltration: State of the art. International Journal of Agricultural and Biological Engineering, 1(1), 22-30.
Machiwal, D., Jha, M. K., & Mal, B. (2006). Modelling infiltration and quantifying spatial soil variability in a wasteland of Kharagpur, India. Biosystems Engineering, 95(4), 569-582.
Mehrabi, F., & Sepaskhah, A. R. (2013). Spatial variability of infiltration characteristics at watershed scale: a case study of bajgah plain. Journal of Agricultural Engineering Research, 14(1), 13-32.
Merdun, H., Çınar, Ö., 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(1–2), 108-116. doi: http://dx.doi.org/10.1016/j.still.2005.08.011
Minasny, B., & McBratney, A. B. (2002). The method for fitting neural network parametric pedotransfer functions. Soil Science Society of America Journal, 66(2), 352-361. doi: 10.2136/sssaj2002.3520
Moosavi, A. A., & Sepaskhah, A. (2011). Artificial neural networks for predicting unsaturated soil hydraulic characteristics at different applied tensions. Archives of Agronomy and Soil Science, 58(2), 125-153. doi: 10.1080/03650340.2010.512289
Moosavizadeh-Mojarad, R., & Sepaskhah, A. R. (2011). Comparison between rice grain yield predictions using artificial neural networks and a very simple model under different levels of water and nitrogen application. Archives of Agronomy and Soil Science, 58(11), 1271-1282. doi: 10.1080/03650340.2011.577423
Nelson, D., & Sommers, L. (1996). Total carbon, organic carbon, and organic matter. In D. Sparks (Ed.), Methods of Soil Analysis: Part III-Physical and Mineralogical Methods (3rd ed., pp. 961-1010). American Society of Agronomy.
Noori, R., Karbassi, A., & Salman Sabahi, M. (2010). Evaluation of PCA and gamma test techniques on ANN operation for weekly solid waste prediction. Journal of Environmental Management, 91(3), 767-771. doi: http://dx.doi.org/10.1016/j.jenvman.2009.10.007
Ogbe, V., Mudiare, O., & Oyebode, M. (2008). Evaluation of furrow irrigation water advance models. Journal of Agricultural Engineering and Technology, 16(1), 74-83.
Pachepsky, Y. A., Timlin, D., & Varallyay, G. (1996). Artificial neural networks to estimate soil water retention from easily measurable data. Soil Science Society of America Journal, 60(3), 727-733. doi: 10.2136/sssaj1996.03615995006000030007x
Parasuraman, K., Elshorbagy, A., & Si, B. C. (2006). Estimating saturated hydraulic conductivity in spatially variable fields using neural network ensembles. Soil Science Society of America Journal, 70(6), 1851-1859. doi: 10.2136/sssaj2006.0045
Philip, J. R. (1957). The theory of infiltration: 1. The infiltration equation and its solution. Soil Science, 83(5), 345-358.
Richter, Q. Y. R. A. J. (1989). A new method for scaling Philip's equation of infiltration. Journal of Hydraulic Engineering, 9, 1-8.
Sablani, S., Ramaswamy, H., Sreekanth, S., & Prasher, S. (1997). Neural network modeling of heat transfer to liquid particle mixtures in cans subjected to end-over-end processing. Food Research International, 30(2), 105-116.
Saxton, K. E., Rawls, W. J., Romberger, J. S., & Papendick, R. I. (1986). Estimating generalized soil-water characteristics from texture. Soil Science Society of America Journal, 50(4), 1031-1036. doi: 10.2136/sssaj1986.03615995005000040039x
Schaap, M. G., Leij, F. J., & van Genuchten, M. T. (1998). Neural network analysis for hierarchical prediction of soil hydraulic properties. Soil Science Society of America Journal, 62(4), 847-855. doi: 10.2136/sssaj1998.03615995006200040001x
Shaalan, K., Riad, M., Amer, A., & Baraka, H. (1999). Speculative work in neural network forecasting: an application to Egyptian cotton production. The Egyptian Computer Journal, 27,  58-76.
Sharma, A., Sahoo, P. K., Tripathi, R., & Meher, L. C. (2016). Artificial neural network-based prediction of performance and emission characteristics of CI engine using polanga as a biodiesel. International Journal of Ambient Energy, 37(6), 559-570.
Sy, N. L. (2006). Modelling the infiltration process with a multi-layer perceptron artificial neural network. Hydrological Sciences Journal, 51(1), 3-20. doi: 10.1623/hysj.51.1.3 
Van Genuchten, M. T. (1980). A closed-form equation for predicting the hydraulic conductivity of unsaturated soils. Soil Science Society of America Journal, 44(5), 892-898.
Zhang, G., Eddy Patuwo, B., & Y. Hu, M. (1998). Forecasting with artificial neural networks:: The state of the art. International Journal of Forecasting, 14(1), 35-62. doi: http://dx.doi.org/10.1016/S0169-2070(97)00044-7
Zhang, G., & Hu, M. Y. (1998). Neural network forecasting of the British Pound/US Dollar exchange rate. Omega, 26(4), 495-506. doi: http://dx.doi.org/10.1016/S0305-0483(98)00003-6