Modeling the Pb Distribution Using Support Vector Machines in Surface Soil of the Lands Surrounding the Dezful-Ahvaz Road

Authors

1 M.Sc. student. Khorasgan Islamic Azad University of Esfahan. Iran

2 Associate Professor, Faculty member and Education Deputy, Islamic Azad University of Esfahan. Iran

3 Assistant Professor., Department of Soil Science, Vali-e-Asr University of Rafsanjan, Iran

Abstract

Investigation of heavy metal distributions in environment is vital for the remediation of contaminated soils and environmental monitoring. In this study, the Pb-concentration variation in surface soils surrounding the Dezful-Ahvaz highway and the soil parameters influencing it were investigated. For this purpose, soil sampling was performed from depth of 0-10 cm of adjacent lines with the highway by collecting three identical samples at intervals of 15, 40, and 100 m from the road side (a total of 135 samples). Then, the total Pb-concentration, organic matter, calcium carbonate equivalent (CCE), pH, electrical conductivity (EC), and soil particle size distribution (clay, silt, sand, fine sand, and very fine) were measured in each soil sample. Modeling analyses were performed using support vector machine (SVM) and multiple linear regression (MLR) methods. To investigate the model performances, some statistical indicators including the correlation coefficient (r), root mean square error (RMSE), and model efficiency factor (MEF) were calculated between the measured and the predicted values. The variable selection results using the SVMs method indicated that the CCE was the most effective factor on Pb- adsorption by soil particles at the distance of 0-15 m from the road side and the clay % and pH (followed by sand %) parameters had the highest importance coefficients at the distances of 15-45 [H1] and 45-100 m, respectively. The r coefficient and MEF values for estimation of Pb using the SVM model were 0.86 and 0.70 %, while they were 0.62 and 0.38 % for the MLR models. Therefore, according to the obtained results in this study, it appears that it would be possible to use support vector machines for determining the factors influencing the Pb- adsorption by soil surrounding the road sides and for modelling heavy metal concentration variations in soils.



 [H1]40؟

Keywords

References

  1. بسالت پور، ع.ا.، ح. شیرانی، و ع. اسفندیارپور. مدل­سازی پایداری خاکدانه ها با استفاده از ماشین­های بردار پشتیبان و رگرسیون چند متغیره خطی. مجله آب و خاک. 1394. 29(2): 406-417.
  2. حمزه، م، ع. نشان­گرهای ژئوشیمیایی و زیست محیطی در محدوده شهری. پایان نامه کارشناسی ارشد. دانشگاه شهید باهنر کرمان. 1385. 387 ص.
  3. سلیمی، م.، امین، م، ا.، ابراهیمی، ا.، قاضی فرد، ا.، نجفی، پ.، امینی، ح.، رزمجو، پ. و وحید دستجردی، ک. تأثیر شوری بر گیاه پالایی کادمیوم از خاک های آلوده. مجله تحقیقات نظام سلامت. 1390. 7 (6): 1130- 1137.
  4. Adrano, D. C.Trace element in the terrestrial environment, Springer Vslley, New York, 1998. 520 pp.
  5. Ayres, A. D., and D.W. Westcot. Water quality for agriculture. F.A.O. Irrigation and Drainage bulletin. 1985. No 29.
  6. Besalatpour A.A., Ayoubi S., Hajabbasi M.A., Gharipour A., and A. Yousefian Jazi. Feature selection using parallel genetic algorithm for the prediction of geometric mean diameter of soil aggregates by machine learning methods. Arid Land Research and Management. 2014. 28: 383-394.  
  7. Besalatpour A.A., Hajabbasi M.A., Ayoubi S., Mosaddeghi M.R., and R. Schulin. Estimating wet soil aggregate stability from easily available data in a highly mountainous watershed. Catena. 2013. 111: 72-79.
  8. Brooks S.C., and Herman J.S. Rate and extent of cobalt sorption to representative aquifer minerals in the presence of a moderately strong ligand. Applied Geochemistry. 1998. 13: 77-88.
  9. Cao, H.F., Chang, A.C., and Page, A.L. Heavy metal contents of sludge treated soils as determined by three extraction procedures. Journal of Environmental Quality. 1984. 13: 632-634.
  10. Chang C, Wang B, Shi L, Li Y, Duo L and Zhang W. Alleviation of salt stress-induced inhibition of seed germination in cucumber (Cucumis sativus L.) by ethylene and glutamate. Journal of Plant Physiology, 2010. 167: 1152-1156.
  11. Coelho, M. C., faras,  T. L. and Rouphail, N.M. "Impact of speed control traffic signals on pollutant emissions", Tranportation Research Part D. 2005. 10: 323- 340.
  12. Doran JW, Parkin TB. Quantitative indicators of soil quality: a minimum data ser. International Journal. Doran W, Jones AJ (Eds), Methods of Assessing Soil Quality. Soil Science society of America. Special Pub. 1996. 49: 25-37
  13. Gee G.W., and Bauder J.W. Particle size analysis. In: Klute, A. (Ed.), Methods of Soil Analysis: Part 1., Agronomy Handbook No 9., American Society of Agronomy and Soil Science Society of America, Madison, WI. 1986. 383-411.
  14. Gomes P.C., Fontes M.P., Dasilva A.G., Mendoca E.S., and Netto A.R. Selectivity sequence and competitive adsorption of heavy metal by Brazilin soils. Soil Science Society of America Journal. 2001. 65: 1115-1121.
  15. Henderson BL, Bui EN, Moran CJ, Simon DAP. Australia-wide predictions of soil properties using decision trees. Geo derma, 2005. 124(3): 383-398.
  16. Kabata A. Trace Element in Soils and Plants. CRC Press, Boca Raton Ann. Arbor. London, 2011.
  17. Kim MJ, Kim TS. A neural classifier with fraud density map for effective credit card fraud detection. In Intelligent Data Engineering and Automated Learning-IDEAL, 2002. 378-383.
  18. Kirkham, M.B. Cadmium in plants on polluted soils: Effects of soil factors, hyperaccumulation, and amendments. Geoderma. 2002. 137: 19-32.
  19. Klute, A. Methods of soil analysis, part I, physical and mineralogical methods. Second edition. Soil Science Society of America INC. Wisconsia. USA. 1986.
  20. Linsay, W.L. Chemical equilibria in soils. John Wiley & Sons, New York. 1979; 412p.
  21. Liao K., Xu S., Wu J, Zhu Q., and An L. Using support vector machines to predict cation exchange capacity of different soil horizons in Qingdao City, China. J. Plant Nutrition Soil Science. 2014. 177(5): 775-782.
  22. Ozkutlu F, Ozturk L, Erdem H, McLaughlin M, CakmakI. Leaf-applied sodium chloride promotes cadmium accumulation in durum wheat grain. Plant and soil. 2007. 290(1-2): 323-31.
  23. Peak, D., G. W. Luther, and D. L. Sparks. ATR-FTIR spectroscopic studies of boric acid adsorption on hydrous ferric oxide. Geochimica Et Cosmochimica Acta. 2003. 67(14): 2551-2560.
  24. Sarkar, B. "Heavy Metals in environment ", NewYork: Marcel Dekker, 2002.
  25. Shirani, H., Habibi, M., Besalatpour A.A., and Esfandiarpour I. Physical quality of calcareous agricultural soils in a semiarid region of Iran: tackling challenges with the affecting parameters using a hybrid PSO-DT algorithm. Geoderma. 2016. 259-260: 1-11.
  26. Singaram, P., LaIsuna, K., and Mahimairaja, S. Metal contamination in urban soil, water environment and remediation strategies. 18th World congress of soil science July 9_15, 2006_ Philadelphia.
  27. Spencer M, Whitfort T, McCullagh J. Mapping dry land salinity using neural networks, AI SpringerVerlag. 2004. 1233-1238.
  28. Twarakavi N.K.C., Simunek J., and Schaap M.G. Development of pedotransfer functions for estimation of soil hydraulic parameters using support vector machines. Soil Science Society American Journal. 2009. 73: 1443-1452.
  29. Vapnik V. Statistical Learning Theory, Wiley, New York. 1998.
  30. Walk ley A, Black IA. An examination of Degtjaref method for determination of soil organic matter and a proposed modification of the chromic acid titration method. Soil Science.1934. Soc. Am. J. 37: 27-29.
  31. Wang L. Support Vector Machines: Theory and Applications. Springer-Ver lag, 2005, New York.
  32. Wang W.C., Chau K.W, Cheng C.T., and Qiu L. A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series. J. Hydrology. 2009. 374: 294-306.
Iranian Journal of Soil Research
Volume 31, Issue 1
June 2017
Pages 143-153

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