Modeling of Groundwater Resources Heavy Metals Concentration Using Soft Computing Methods: Application of Different Types of Artificial Neural Networks

الموضوعات :

Meysam Alizamir ¹ (Young Researchers & Elite Club, Hamedan Branch, Islamic Azad University, Hamedan, Iran)
Soheil Sobhanardakani ² (Department of the Environment, College of Basic Sciences, Hamedan Branch, Islamic Azad University, Hamedan, Iran)
Lobat Taghavi ³ (Department of the Environmental Pollution, College of Environment and Energy, Science and Research Branch, Islamic Azad University, Tehran, Iran)

تاريخ الإرسال : 12 الأحد , ذو الحجة, 1438 تاريخ التأكيد : 20 الإثنين , صفر, 1440 تاريخ الإصدار : 12 الأحد , ذو الحجة, 1438

الکلمات المفتاحية: Radial Basis Function, groundwater resources, Heavy metals concentration, Multi-layer percep-tron, Asadabad Plain,

ملخص المقالة :

Nowadays, groundwater resources play a vital role as a source of drinking water in arid and semiarid regions and forecasting of pollutants content in these resources is very important. Therefore, this study aimed to compare two soft computing methods for modeling Cd, Pb and Zn concentration in groundwater resources of Asadabad Plain, Western Iran. The relative accuracy of several soft computing models, namely multi-layer perceptron (MLP) and radial basis function (RBF) for forecasting of heavy metals concentration have been investigated. In addition, Levenberg-Marquardt, gradient descent and conjugate gradient training algorithms were utilized for the MLP models. The ANN models for this study were developed using MATLAB R 2014 Software program. The MLP performs better than the other models for heavy metals concentration estimation. The simulation results revealed that MLP model was able to model heavy metals concentration in groundwater resources favorably. It generally is effectively utilized in environmental applications and in the water quality estimations. In addition, out of three algorithms, Levenberg-Marquardt was better than the others were. This study proposed soft computing modeling techniques for the prediction and estimation of heavy metals concentration in groundwater resources of Asadabad Plain. Based on collected data from the plain, MLP and RBF models were developed for each heavy metal. MLP can be utilized effectively in applications of prediction of heavy metals concentration in groundwater resources of Asadabad Plain.

المصادر:

1. Sarala Thambavani D., Uma Mageswari T.S.R., 2013. Metal pollution assessment in ground water. Bull Environ Pharm Life Sci. 2(12), 122-129.
2. Srinivas J., Purushotham A.V., Murali Krishna K.V.S.G., 2013. Determination of water quality index in industrial areas of Kakinada, Andhra Pradesh, India. Int Res J Environ Sci. 2(5), 37-45.
3. Yari A.R., Siboni M.S., Hashemi S., Alizadeh M., 2013. Removal of heavy metals from aqueous solutions by natural adsorbents (A review). Arch Hyg Sci. 2(3), 114-124.
4. Zhu F., Fan W., Wang X., Qu L., Yao S., 2011. Health risk assessment of eight heavy metals in nine varieties of edible vegetable oils consumed in China. Food Chem Toxicol. 49, 3081-3085.
5. Chen X., Wang K., Wang Z., Gan C., He P., Liang Y., Jin T., Zhu G., 2014. Effects of lead and cadmium co-exposure on bone mineral density in a Chinese population. Bone. 63, 76-80.
6. Hellström L., Persson B., Brudin L., Petersson Grawé K., Öborn I., Järup L., 2007. Cadmium exposure pathways in a population living near a battery plant. Sci Total Environ. 373, 447-455.
7. Pérez A.L., Anderson K.A., 2009. DGT estimates cadmium accumulation in wheat and potato from phosphate fertilizer applications. Sci Total Environ. 407, 5096-5103.
8. Krejpcio Z., Sionkowski S., Bartela J., 2005. Safety of fresh fruits and juices available on thePolish market as determined by heavy metal residues. Polish J Environ Stud. 14(6), 877-881.
9. Peralta-Videa J.R., Lopez M.L., Narayan M., Saupe G., Gardea-Torresdey J., 2009. The biochemistry of environmental heavy metal uptake by plants: Implications for the food chain. Int J Biochem Cell Biol. 41, 1665-1677.
10. Minh N.D., Hough R.L., Thuy L.T., Nyberg Y., Mai L.B., Vinh N.C., Khaim N.M., Öborn I., 2012. Assessing dietary exposure to cadmium in a metal recycling community in Vietnam: Age and gender aspects. Sci Total Environ. 416, 164-171.
11. Zhang W.L., Du Y., Zhai M.M., Shang Q., 2014. Cadmium exposure and its health effects: A 19-year follow-up study of a polluted area in China. Sci Total Environ. 470-471, 224-228.
12. Chen H., Teng Y., Lu S., Wang Y., Wang J., 2015. Contamination features and health risk of soil heavy metals in China. Sci Total Environ. 512-513, 143-153.
13. Lin L.Q., Cong L., Yun W.H., Yang J., Ming H., Wan Z.B., Kai Ch., Lei H., 2015. Association of soil cadmium contamination with ceramic industry: A case study in a Chinese town. Sci Total Environ. 514, 26-32.
14. Matovic V., Buha A., Ðukić-Ćosić D., Bulat Z., 2015. Insight into the oxidative stress induced by lead and/or cadmium in blood, liver and kidneys. Food Chem Toxicol. 78, 130-140.
15. Cherfi A., Abdoun S., Gaci O., 2014. Food survey: Levels and potential health risks of chromium, lead, zinc and copper content in fruits and vegetables consumed in Algeria. Food Chem Toxicol. 70, 48-53.
16. Moriarity R.S., Harris J.T., Cox R.D., 2014. Lead toxicity as an etiology for abdominal pain in the emergency department. J Emerg Med. 46, e35-e38.
17. Rahman M.A., Rahman M.M., Reichman S.M., Lim R.P., Naidu R., 2014. Heavy metals in Australian grown and imported rice and vegetables on sale in Australia: Health hazard. Ecotoxicol Environ Safe. 100, 53-60.
18. Narin I., Tuzen M., Sari H., Soylak M., 2005. Heavy metal content of potato and corn chips from Turkey. Bull Environ Contam Toxicol. 74, 1072-1077.
19. Öztürk E., Atsan E., Polat T., Kara K., 2011. Variation in heavy metal concentrations of potato (Solanum tuberosum L.) cultivars. J Animal Plant Sci. 21(2), 235-239.
20. Sobhanardakani S., Jamali M., Maànijou M., 2014. Evaluation of As, Zn, Cr and Mn concentrations in groundwater resources of Razan Plain and preparing the zoning map using GIS. J En-viron Sci Technol. 16(2), 25-38. (In Persian)
21. Fazel Tavasol S., Vusuq B.P., Manshuri M., 2010. The investigation of heavy metal (Sn-Pb) concentration in ground water resources and their environmental effects, Case study: North Chardoly Plain, West of Iran. Proceedings of the 1st International Applied Geological Congress; Apr 26-28; Mashhad, Iran. p. 26-28.
22. Hattab N., Hambli R., Motelica-Heino M., Bourrat X., Mench M., 2013. Application of neural network model for the prediction of chromium concentration in phytoremediated contaminated soils. J Geochem Explor. 128, 25-34.
23. Keskin T.E., Dugenci M., Kacaroglu F., 2015. Prediction of water pollution sources using artificial neural networks in the study areas of Sivas, Karabuk and Bartın (Turkey). Environ Earth Sci. 73(9), 5333-5347.
24. Podder M.S., Majumder C.B., 2016. The use of artificial neural network for modelling of phycoremediation of toxic elements As(III) and As(V) from wastewater using Botryococcus braunii. Spectrochim Acta A Mol Biomol Spectrosc. 155, 130-145.
25. Mandal S., Mahapatra S.S., Sahu M.K., Patel R.K., 2015. Artificial neural network modeling of As(III) removal from water by novel hybrid material. Process Safety Environ Protect. 93, 249-264.
26. Mohamad Nor A.S., Faramarzi M., Md Yunus M.D., Ibrahim S., 2014. Nitrate and sulfate estimations in water sources using a planar electromagnetic sensor array and artificial neural network method. IEEE Sensor J. 15(1), 497-504.
27. Hornik K., Stinchcombe M., White H., 1990. Uni-versal approximation of an unknown mapping and its
28. derivatives using multilayer feedforward networks. Neur Net. 3(5), 551-560.
29. Govindaraju R.S., 2000. Artificial neural networks in hydrology. I: Preliminary concepts. J Hydrol Eng. 5(2), 115-123.
30. Daliakopoulos I.N., Coulibaly P., Tsanis I.K., 2005. Groundwater level forecasting using artificial neural networks. J Hydrol. 309(1-4), 229-240.
31. Haykin S., 1998. Neural Networks: A Comprehensive Foundation. New Jersey: Prentice-Hall, Upper Saddle River. 88-92.
32. Sudheer K.P., Gosain A.K., Ramasastri K.S., 2002. A data-driven algorithm for constructing artificial neural network rainfall-runoff models. Hydrol Process. 16(6), 1325-1330.
33. Lin J.Y., Cheng C.T., Chau K.W., 2006. Using support vector machines for long-term discharge prediction. Hydrol Sci J. 51(4), 599-612.
34. Hecht-Nielsen R., 1987. Kolmogorovââ‚‌™s mapping neural network existence theorem. Proceedings of the International Symposium on Neural Networks; New York, USA.
35. Keskin T.E., Dugenci M., Kacaroglu F., 2015. Prediction of water pollution sources using artificial neural networks in the study areas of Sivas, Karabuk and Bartın (Turkey). Environ Earth Sci. 73(9), 5333-5347.
36. Nor A.S.M., Faramarzi M., Yunus M.A.M., Ibrahim S., 2015. Nitrate and sulfate estimations in water sources using a planar electromagnetic sensor array and artificial neural network method. IEEE Sensors J. 15(1), 497-504.