Abstract

The aim of this paper is to design artificial neural network as an alternative accurate tool to estimate concentration of Cadmium in contaminated soils for any depth and time. First, fifty soil samples were harvested from a phytoremediated contaminated site located in Qanat Aljaeesh in Baghdad city in Iraq. Second, a series of measurements were performed on the soil samples. The inputs are the soil depth, the time, and the soil parameters but the output is the concentration of Cu in the soil for depth x and time t. Third, design an ANN and its performance was evaluated using a test data set and then applied to estimate the concentration of Cadmium. The performance of the ANN technique was compared with the traditional laboratory inspecting using the training and test data sets. The results of this work show that the ANN technique trained on experimental measurements can be successfully applied to the rapid estimation of Cadmium concentration.

References

1. Yetilmezsoy, K., and Demirel, S., (2008), Artificial neural network (ANN) approach for modeling of Pb(II) adsorption from aqueous solution by Antep pistachio (Pistacia Vera L.) shells, Journal of Hazardous Materials,Vol.153, pp1288-1300.
2. Adriano, D.C., (2001), Trace elements in terrestrial environments; Biochemistry, bioavailability and risks of metals. Springer-Verlag, New York.
3. Minasny, B., McBratney, A. B., (2002), The neuro-m methods for fitting neural network parametric pedotransfer functions. Soil Science Society of America Journal 66, 352-361
4. Schug, B., Düring, R.A., Gäth, S., (2000), Improved cadmium sorption isotherms by the determination of initial contents using the radioisotope 109 Cd. Journal of plant nutrition and soil science 163, 197–202
5. Behrens, T., Förster, H., Scholten, T., Steinrüken, U., Spies, E., Goldschmitt, M., (2005), Digital soil mapping using artificial neural networks. Journal of plant nutrition and soil science 168, 21-33
6. Buszewski, B., Kowalkowski, T., (2006), A new model of heavy metal transport in the soil using non-linear artificial neural networks. Journal of environmental engineering science 23 (4), 589-595
7. Anagu, I., Ingwersen, J., Utermann, J., Streck, T., (2009), Estimation of heavy metal sorption in German soils using artificial neural networks. Geoderma 152, 104–112
8. Sarmadian, F., Taghizadeh Mehrjardi, R., (2008), Modeling of Some Soil Properties Using Artificial Neural Network and Multivariate Regression in Gorgan Province, North of Iran, Global Journal of Environmental Research 2 (1), 30-35
9. Hambli, R., (2009), Statistical damage analysis of extrusion processes using finite element method and neural networks simulation. Finite Elements in Analysis and Design -45- 10, 640- 649
10. Gandhimathi, A., Meenambal, T., (2012), Analysis of Heavy Metal for Soil in Coimbatore by using ANN Model. European Journal of Scientific Research 68, (4), 462-474.
11. Minasny, B., Hopmans, J.W., Harter, T., Eching, S.O., Tuli, A., Denton, M.A., (2004), Neural networks prediction of soil hydraulic functions for alluvial soils using multistep outflow data. Soil Science Society of America Journal 68, 417—429.
12. Hambli, R., Chamekh, A., Bel Hadj Salah, H., (2006), Real-time deformation of structure using finite element and neural networks in virtual reality applications, Finite Elements in Analysis and Design 42, (11), 985-991.
13. R. D. Khonde & S. L. Pandharipande, (2011), Application of Artificial Neural Network for Standardization of Digital Colorimeter‖, International Journal of Computer Applications, ICCIA-5, pp 1-4.
14. Tawfiq, L. N. M. and Oraibi, Y. A., (2013), Design Feed forward Neural Networks for Solving Ordinary Initial Value, LAP LAMBERT Academic Publishing.
15. Pandharipande, S. L., Anish M. Shah & Heena Tabassum, (2012), Artificial Neural Network Modeling for Estimation of Composition of a Ternary Liquid Mixture with its Physical Properties such as Refractive Index, pH and Conductivity, International Journal of Computer Applications, Vol. 45, No. 9, pp 26-29.
16. Pandharipande, S. L., and Singh, A., (2012), ―Optimizing topology in developing artificial neural network model for estimation of hydrodynamics of packed column‖, International Journal of Computer Applications, Vol. 58, No. 3, pp 49-53.
17. Khonde, R. D., and Pandharipande, S. L., (2012), ―Artificial Neural Network modeling for adsorption of dyes from aqueous solution using rice husk carbon‖, International Journal of Computer Application, Vol. 41, No.4, pp 1-5.
18. Pandharipande, S., and Shah, A. M., (2012), Modeling combined VLE of four quaternary mixtures using artificial neural network, International Journal of Advances in Engineering, Science and Technology (IJAEST), Vol. 2, No. 2, pp 169-177.
19. Pandharipande, S. L., Akheramka, A., Singh, A., and Shah, A., (2012), Artificial Neural Network Modeling of Properties of Crude Fractions with its TBP and Source of Origin and Time‖, International Journal of Computer Application, Vol. 52, No.15, pp 20-25.
20. Mandavgane, S. A., Pandharipande, S. L., and Subramanian, D., (2006), Modeling of desilication of green liquor using artificial neural network, International journal of chemical technology, Vol. 13, pp 168-172.
21. Godini, H.R., Ghadrdan, M., Omidkhah, M.R., and Madaeni, S.S., (2011), ―Part II: Prediction of the dialysis process performance using Artificial Neural Network (ANN)‖, Desalination, Vol. 265, pp 11-21
22. Al-Adili, A. S., (1998), Geotechnical Evaluation of Baghdad Soil Subsidence and their Treatments,‖ Thesis, University of Baghdad.
23. Bloemen, M. L., Markert, B., and Lieth, H., (1995), The Distribution of Cd, Cu, Pb And Zn in Topsoils of Osnabrück in Relation to Land Use,‖ The Science of the Total Envi-ronment, Vol. 166, No. 1-3, , pp.137-148. doi:10.1016/0048-9697(95)04520-B
24. Kabata-Pendias, A., and Pendias, H., (2001), Trace Element in Soils and Plants, CRC Press, London.
25. Adriano, D. C., (2001), Trace Elements in Terrestrial Environments: Biogeochemistry, Bioavailability and Risks of Metals, Springer-Verlag, New York.
26. Volensky, B., (1990), Removal and Recovery of Heavy Metals by Biosorption, In: Biosorption of Heavy Metals, CEC Press, Boston.
27. Kisku, G. C., Barman, S. C. and Bhrgava, S. K., (2000), Contamination of Soils and Plants with Potentially Toxic Elements Irrigated with Mixed Industrial Effluent and Its Impact on the Environment, Water, Air & Soil Pollution, Vol. 120, No. 1-2, , pp. 121-137. doi:10.1023/A:1005202304584

Download PDF
Back