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بهبود برآورد ظرفیت تبادل کاتیونی خاک با استفاده از ابعاد فرکتالی | ||
تحقیقات آب و خاک ایران | ||
دوره 51، شماره 12، اسفند 1399، صفحه 3102-3087 اصل مقاله (1.05 M) | ||
نوع مقاله: مقاله پژوهشی | ||
شناسه دیجیتال (DOI): 10.22059/ijswr.2020.308515.668711 | ||
نویسندگان | ||
حسن مظفری؛ سید علی اکبر موسوی* ؛ فرناز احمدی | ||
گروه علوم خاک، دانشکده کشاورزی، دانشگاه شیراز، شیراز، ایران | ||
چکیده | ||
ظرفیت تبادل کاتیونی[1] (CEC) یکیاز مهمترین ویژگیهای شیمیایی خاک از نظر تغذیه گیاه و جذب سطحی آلایندهها در خاک است که اندازهگیری آن زمانبر و پرهزینه است. بنابراین این پژوهش با هدف برآوردCEC خاک با استفاده از مقادیر ماده آلی، اجزای بافت خاک و ابعاد فرکتالی تایلر و ویتکرفت (DT) و سپاسخواه و تافته (DS) و همچنین بررسی کارایی ابعاد فرکتالی ذکر شده بهعنوان یک متغیر مستقل و تأثیر آن بر دقت روابط رگرسیونی پیشبینی CEC خاک انجام شد. در این پژوهش از دادههای 100 نمونه خاک مربوط به بانک اطلاعات خاک UNSODA[2]استفاده شد. توزیع اندازه ذرات اولیه خاک با استفاده از روش اسکگز و بعد فرکتالی اندازه ذرات اولیه خاک نیز با استفاده از دو روش پیشنهادی سپاسخواه و تافته و تایلر و ویتکرفت محاسبه شد. نتایج نشان داد که مقادیر CEC دارای ارتباط منفی معنیدار با مقدار شن و ارتباط مثبت معنیدار با مقادیر لگاریتم (در پایه 10) ماده آلی، رس، DS و DT داشت. مقادیر ضرایب تبیین دادههای آموزش و آزمون، ریشه میانگین مربعات خطای نرمال شده[3] (درصد) و ضریب نش- ساتکلیف برای ارتباط رگرسیون چند متغیره بین CEC با لگاریتم (در پایه 10) ماده آلی و رس بهترتیب برابر با 77/0، 84/0، 2/17 و 92/0؛ بین CEC با لگاریتم (در پایه 10) ماده آلی و DS بهترتیب برابر با 77/0، 85/0، 2/17 و 92/0 و بین CEC با لگاریتم (در پایه 10) ماده آلی و DT بهترتیب برابر با 77/0، 87/0، 0/14 و 93/0 بودند. بنابراین بیشترین دقت روابط رگرسیونی با ورود متغیرهای مستقل لگاریتم (در پایه 10) ماده آلی و DT حاصل شد و استفاده از بعد فرکتالیDT سبب افزایش دقت تخمینها شد. [1] Cation exchange capacity (CEC) [2] Unsaturated soil hydraulic database (UNSODA) [3] Normalized root mean square error (NRMSE) | ||
کلیدواژهها | ||
بعد فرکتال تایلر و ویتکرفت؛ بعد فرکتال سپاسخواه و تافته؛ توزیع اندازه ذرات اولیه خاک؛ رس؛ ماده آلی | ||
مراجع | ||
Alfaro Soto, M.A., Chang, H.K. and van Genuchten M.Th. (2017). Fractal-based models for the unsaturated soil hydraulic functions. Geoderma, 306, 144-151. Bannayan, M. and Hoogenboom, G. (2009). Using pattern recognition for estimating cultivar coefficients of a crop simulation model. Field Crops Research, 111, 290-302. Bariklo, A., Alamdari, P. and Nikbakht, J. (2018). Comparison of artificial neural network and regression pedotransfer functions for estimation of soil cation exchange capacity in Tabriz plain. Applied Soil Research, 8(1), 174-186. (In Farsi) Deng, Y., Cai, C., Xia, D., Ding, S. and Chen, J. (2017). Fractal features of soil particle size distribution under different land-use patterns in the alluvial fans of collapsing gullies in the hilly granitic region of southern China. PLOS One, 12(3), 1-21. Ersahin, S., Gunal, H., Kutlu, T., Yetgin, B. and Coban, S. (2006). Estimating specific surface area and cation exchange capacity in soils using fractal dimension of particle-size distribution. Geoderma, 136(3), 588-597. Esmaeelnejad, L., Seyedmohammadi, J., Shabanpour, M. and Ramezanpour H. (2014). Prediction of specific surface area and cation exchange capacity using fractal dimension of soil particle size distribution. Iranian Journal of Soil and Water Research, 45(4), 463-474. (In Farsi) Feng, Y., Cui, N., Gong, D., Zhang, Q. and Zhao, L. (2017). Evaluation of random forests and generalized regression neural networks for daily reference evapotranspiration modelling. Agricultural Water Management, 193, 163-173. Fooladmand, H. R. (2008a). Estimation of cation exchange capacity from some soil physic-chemical properties. Journal of Agricultural Sciences and Natural Resources, 15(1), 11-18. (In Farsi) Fooladmand, H. R. (2008b). Estimating cation exchange capacity using soil textural data and soil organic matter content: A case study for the south of Iran. Archives of Agronomy and Soil Science, 54(4), 381-386.
Fooladmand, H. R, Sepaskhah, A. R. (2006). Improved estimation of the soil particle-size distribution from textural data. Biosystems Engineering, 94, 133-138. Foroughifar, H., Jafarzadah, A. A., Torabi Gelsefidi, H., Aliasgharzadah, N., Toomanian, N. and Davatgar, N. (2010). Spatial variations of surface soil physical and chemical properties on different landforms of Tabriz plain. Water and Soil Science, 21(3), 1-21. (In Farsi) Ghanbarian, B. and Daigle, H. (2015). Fractal dimension of soil fragment mass-size distribution: A critical analysis. Geoderma, 245-246, 224-232. Ghanbarian-Alavijeh, B. and Millán, H. (2009). The relationship between surface fractal dimension and soil water content at permanent wilting point. Geoderma, 151(3), 224-232. Huang, G. and Zhang, R. (2005). Evaluation of soil water retention curve with the pore-solid fractal model. Geoderma, 127, 52-61. Hunt, A. G., Ghanbarian, B. and Saville, K. C. (2013). Unsaturated hydraulic conductivity modeling for porous media with two fractal regimes. Geoderma, 207, 268-278. Hwang, S. I. and Hong, S. P. (2006). Estimating relative hydraulic conductivity from lognormally distributed particle-size data. Geoderma, 133, 421-430. Jafarzadeh, A. A., Pal, M., Servati, M., Fazeli Fard, M. H. and Ghorbani, M. A. (2016). Comparative analysis of support vector machine and artificial neural network models for soil cation exchange capacity prediction. International Journal of Environmental Science and Technology, 13, 87-96. Karimi, S. A., Davari, M. and Babaeian, E. (2017). Deriving and assessing spectrotransfer function and pedotransfer function in predicting soil cation exchange capacity. Iranian Journal of Soil Research, 31(4), 641-654. Keller, A., von Steiger, B., van der Zee, S. T. and Schulin, R. (2001). A stochastic empirical model for regionalheavy-metal balances inagroecosystems. Journal of Environmental Quality, 30, 1976–1989. Kravchenko, A. and Zhang, R. (1998). Estimating the soil water retention from particle-size distribution: A fractal approach. Soil Science, 163(3), 171-179. Mahallati, S. Z., Pazira, E., Abbasi, F. and Babazadeh, H. (2018). Estimation of Soil Water Retention Curve Using Fractal Dimension. Journal of Applied Sciences and Environmental Management, 22(2), 173-178. Mehrabanian, M., Taghizadeh Mehrjardi, R. and Dehghani, F. (2010). Assessing the efficiency of pedotransfer functions for estimating CEC in some calcareous and gypsiferous soils of Yazd province. Journal of Water and Soil Conservation, 17(1), 113-127. (In Farsi) Memarian Fard, M. and Beigi Harchagani, H. (2009). Comparison of artificial neural network and regression pedotransfer functions models for prediction of soil cation exchange capacity in Chaharmahal- Bakhtiari province. Journal of Water and Soil, 23(4), 90-99. (In Farsi) Mohammadi. J. (2010). Pedometrics, volume 13 (Fractal Theory). Pelk publishers, 383 p. (In Farsi) Momtaz, H. R., Jafarzadeh, A. A., Torabi, H., Oustan, S., Samadi, A., Davatgar, N. and Gilkes, R. J. (2009). An assessment of the variation in soil properties within and between landform in the Amol region, Iran. Geoderma, 149(1), 10-18. Moosavi, A. A. and Sepaskhah, A. R. (2012a). Spatial variability of physico-chemicalproperties and hydraulic characteristics of a gravelly calcareous soil. Archives of Agronomy and Soil Science, 58(6), 631-656. Moosavi, A. A. and Sepaskhah, A. R. (2012b). Artificial neural networks for predicting unsaturated soil hydraulic characteristics at different applied tensions. Archives of Agronomy and Soil Science, 58, 125-153. Moosavi, A. A. and Sepaskhah, A. R. (2013). Sorptive number prediction of highly calcareous soils at different applied tensions using regression models. Plant Knowledge Journal, 2(2), 62-68. Mousavi, F., Abdi, E., Ghalandarzadeh, A., Bahrami, H. A., Majnounian, B. and Mirzaei, S. (2018). Estimate of soil cation exchange capacity using reflectance spectrometry. Journal of Forest Research and Development, 4(3), 347-361. (In Farsi) Mozaffari, H. and Moosavi, A. A. (2020). Estimating cation exchange capacity of calcareous soils using the fractal dimension of particles. In: Proceedings of 6th National Conference on Strategic Research in Chemistry and Chemical Engineering, 1 Jan., Shahid Beheshti University, Tehran, Iran, pp. 1-6. (In Farsi) Mozaffari, H., Moosavi, A. A. and Sepaskhah, A. R. (2019). Effect of land use on of some physical and chemical properties of a calcareous soil. Iranian Journal of Soil Research, 33(4), 525-541. (In Farsi) Omidifar, M. and Moosavi, A. A. (2015). Prediction of some hydraulic properties of calcareous soils of bajgah region fars province using regression pedotransfer functions. Iranian Journal of Soil Research, 29(1), 83-92. (In Farsi) Ostovari, Y. and Beigi Harchegani, H. (2013). Pedotransfer functions for estimating soil volumetric moisture content based on soil fractal dimension. Journal of Water and Soil, 27(3), 630-641. (In Farsi) Rasoulzadeh, A., Razavi, S. and Neyshaboori M. R. (2012). Evaluating the accuracy of methods of estimating saturated hydraulic conductivity in different soils. Journal of Water Research in Agriculture, 26(3), 303-316. (In Farsi) Razali, N. M. and Wah, Y. B. (2011). Power comparisons of shapiro-wilk, kolmogorov-smirnov, lilliefors and anderson-darling tests. Journal of Statistical Modeling and Analytics, 2(1), 21-33. Rezaei Abajelu, E. and Zeinalzadeh, K. (2017). Two and three-phases fractal models application in soil saturated hydraulic conductivity estimation. Journal of Water and Soil, 30(6), 1905-1917. Sadeghi, M., Izadi, A. and Ghahraman, B. (2011). Estimating unsaturated hydraulic conductivity based on fractal geometry. Iranian Journal of Irrigation and Drainage, 5(1), 43-49. (In Farsi) Saidian, M., Godinez, L. J. and Prasad, M. (2016). Effect of clay and organic matter on nitrogen adsorption specific surface area and cation exchange capacity in shales (mudrocks). Journal of Natural Gas Science and Engineering, 33, 1095-1106. Sedaghat, A., Bayat, H. and Safari Sinegani, A. A. (2016). Estimation of soil saturated hydraulic conductivity by artificial neural networks ensemble in smectitic soils. Eurasian Soil Science, 49(3), 347-357. Sepaskhah, A. R., Tabarzad, A. and Fooladmand, H. R. (2010). Physical and empirical models for estimation of specific surface area of soils. Archives of Agronomy and Soil Science, 56(3), 325-335. Sepaskhah, A. R. and Tafteh, A. (2013). Pedotransfer function for estimation of soil-specific surface area using soil fractal dimension of improved particle-size distribution. Archives of Agronomy and Soil Science, 59(1), 93-103. Seyedmohammadi, J., Esmaeelnejad, L. and Ramezanpour, H. (2016). Determination of a suitable model for prediction of soil cation exchange capacity. Modeling Earth Systems and Environment, 2(156), 1-12. Shirazi, M. A. and Boersma, L. (1984). A unifying quantitative analysis of soil texture. Soil Science Society of America Journal, 48, 142–147. Skaggs, T. H., Arya, L. M., Shouse, P. J. and Mohanty, B. P. (2001). Estimating particle-size distributionfrom limited soil texture data. Soil Science Society of America Journal, 65, 1038-1044. Taghizadeh Mehrjardi, R., Mahmoodi, S. H., Heidari, A. and Akbarzadeh, A. (2009). Prediction of cation exchange capacity using artificial neural network and multivariate regression in Khezrabad region. Journal of Research in Agricultural Science, 5(1), 1-11. (In Farsi) Tang, L., Zeng, G. M., Nourbakhsh, F. and Shen, G. L. (2009). Artificial neural network approach for predicting cation exchange capacity in soil based on physico-chemical properties. Environmental Engineering Science, 26, 137-146. Tyler, S. W. and Wheatcraft, S. W. (1992). Fractal scaling of soil particle-size distributions: analysis and limitations. Soil Science Society of America Journal, 56(2), 362-369. Ulusoy, Y., Tekin, Y., Tumsavas‚ Z. and Mouazen, A. M. (2016). Prediction of soil cation exchange capacity using visible and near infrared spectroscopy. Biosystems Engineering, 52, 72-93. Wilding, L. P. (1985). Spatial variability: its documentation, accommodation and implication to soil surveys. In Soil Spatial Variability. Workshop (pp. 166-194). Xu, Y. (2004). Calculation of unsaturated hydraulic conductivity using a fractal model for the pore-size distribution. Computers and Geotechnics, 31(7), 549-557. Xu, Y. and Dong, P. (2004). Fractal approach to hydraulic properties in unsaturated porous media. Chaos, Solitons & Fractals, 19(2), 327-337. Yilmaz, I. (2006). Indirect estimation of the swelling percent and a newclassification of soils depending on liquid limit and cation exchangecapacity. Engineering Geology, 85, 295-301. Zhou, A., Fan, Y., Cheng, W. and Zhang, J. (2019). A Fractal Model to Interpret Porosity Dependent Hydraulic Properties for Unsaturated Soils. Advances in Civil Engineering, 2019, 1-13. | ||
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