Advertisement

KSCE Journal of Civil Engineering

, Volume 21, Issue 1, pp 134–140 | Cite as

Artificial neural network for modeling nitrate pollution of groundwater in marginal area of Zayandeh-rood River, Isfahan, Iran

  • Kaveh Ostad-Ali-AskariEmail author
  • Mohammad Shayannejad
  • Hossein Ghorbanizadeh-Kharazi
Environmental Engineering

Abstract

Excessive use of chemical fertilizers, especially nitrogen fertilizers to increase crop and improper purification, and delivery of municipal and industrial wastewater are proposed as factors that increase the amount of nitrate in groundwater in this area. Thus, investigation of nitrate contamination as one of the most important environmental problems in groundwater is necessary. In the present study, modeling and estimation of nitrate pollution in groundwater of marginal area of Zayandeh-rood River, Isfahan, Iran, was investigated using water quality and artificial neural networks. 100 wells (77 agriculture well, 13 drinking well and 10 gardens well) in the marginal area of Zayandeh-rood River, Isfahan, Iran were selected. MATLAB software and three-layer Perceptron network were used. The back-propagation learning rule and sigmoid activation function were applied for the training process. After frequent experiments, a network with one hidden layer and 19 neurons make the least error in the process of network training, testing and validation. ANN models can be applied for the investigation of water quality parameters.

Keywords

artificial neural network contamination groundwater nitrates 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Acutis, M., Ducco, G., and Grignani, C. (2000). “Stochastic use of the LEACHN model to forecast nitrate leaching in different maize cropping systems.” Eur. J. Agron., Vol. 13, No. 2–3, pp. 191–206.CrossRefGoogle Scholar
  2. Babiker, I. S., Mohamed, M. A., Terao, H., Kato, K., and Ohta, K. (2004). “Assessment of groundwater contamination by nitrate leaching from intensive vegetable cultivation using geographical information system.” Environ. Int., Vol. 29, No. 8, pp. 1009–1017.CrossRefGoogle Scholar
  3. Bruton, J. M., McClendon, R. W., and Hoogenboom, G. (2000). “Estimating daily pan evaporation with artificial neural network.” Trans. ASAE, Vol. 43, No. 2, pp. 492–496.CrossRefGoogle Scholar
  4. Dorsch, M. M., Scragg, R. K. R., Mcmichael, A. J., Baghurst, P. A., and Dyer, K. F. (1984). “Congenital malformations and maternal drinking water supply in rural South Australia: A case control study.” Am. J. Epidemiol., Vol. 119, No. 4, pp. 473–486.Google Scholar
  5. El-Sadek, A., Feyen, J., and Ragab, R. (2002). “Simulation of nitrogen balance of maize field under different drainage strategies using the DRAINMOD-N model.” Irrig. Drain., Vol. 51, pp. 61–75.CrossRefGoogle Scholar
  6. French, M. N., Krayewski, W. F., and Cuykendall, R. R. (1992). “Rainfall forecasting in space and time using a neural networks.” J. Hydrol., Vol. 137, No. 1–3, pp. 1–37.CrossRefGoogle Scholar
  7. Hambright, K. D., Ragep, F. J., and Ginat, J. (2006). Water in the middle east: Cooperation and technological solutions in the jordan valley, University of Oklahoma Press.Google Scholar
  8. Jain, S. K., Das, A., and Srivastava, D. K. (1999). “Application of ANN for reservoir inflow prediction and operation.” J. Water Res. Plan. Manage., Vol. 125, No. 5, pp. 263–271.CrossRefGoogle Scholar
  9. Keskin, T. E., Düenci, M., and Kaçarolu, F. (2015). “Prediction of water pollution sources using artificial neural networks in the study areas of Sivas, Karabük and Bartn (Turkey).” Environmental Earth Sciences, Vol. 73, No. 9, pp. 5333–5347.CrossRefGoogle Scholar
  10. Khosravi Dehkordi, A., Afyuni, M., and Musavi, F. (2004). “Nitrate concentration in groundwater in the Zayanderoud river basin.” Environmental. Studies. J., Vol. 32, No. 39, pp. 33–40.Google Scholar
  11. Kumar, M., Raghuwanshi, N. S., Singh, R., Wallender, W. W., and Pruitt, W. O. (2002). “Estimating evapotranspiration using artificial neural networks.” J. Irrig. And Drain. ASCE, Vol. 128, No. 4, pp. 224–233.CrossRefGoogle Scholar
  12. Lek, S., Guiresse, M., and Giraudel, J. L. (1999). “Predicting stream nitrogen concentration from watershed features using neural networks.” Water Res., Vol. 33, No. 16, pp. 3469–3478.CrossRefGoogle Scholar
  13. Noh, H., Zhang, Q., Shin, B., Han, S., and Feng, L. (2006). “A neural network model of maize crop nitrogen stress assessment for a multispectral imaging sensor.” Biosyst. Eng., Vol. 94, No. 4, pp. 477–485.CrossRefGoogle Scholar
  14. Nor, A. S. M., Faramarzi, M., Yunus, M. A. M., and Ibrahim, S. (2014). “Nitrate and sulfate estimations in water sources using a planar electromagnetic sensor array and artificial neural network method.” IEEE, Vol. 15, No. 1, pp. 497–504.Google Scholar
  15. Odhiambo, L. O., Yoder, R. E., Yoder, D. C., and Hines, J. W. (2001). “Optimization of fuzzy evaporation model through neural training with input-output examples.” Trans. ASAE, Vol. 44, No. 6, pp. 1625–1633.CrossRefGoogle Scholar
  16. Panagopoulos, Y., Makropoulos, C., Baltas, E., and Mimikou, M. (2011). “SWAT parameterization for the identification of critical diffuse pollution source areas under data limitations.” Ecol. Model., Vol. 222, No. 19, pp. 3500–3512.CrossRefGoogle Scholar
  17. Park, J., Daniels, H. V., and Cho, S. H. (2013). “Nitrite toxicity and methemoglobin changes in southern flounder, paralichthys lethostigma, in brackish water.” J. World. Aquacult. Soc., Vol. 44, No. 5, pp. 726–734.CrossRefGoogle Scholar
  18. Park, Y. S., Cereghino, R., Compin, A., and Lek, S. (2003). “Applications of artificial neural networks for patterning and predicting aquatic insect species richness in running waters.” Ecol. Model., Vol. 160, No. 3, pp. 265–280.CrossRefGoogle Scholar
  19. Rogers, L. L. and Dowla, F. U. (1994). “Optimization of groundwater remediation using artificial neural networks with parallel solute transport modeling.” Water Resour. Res., Vol. 30, No. 2, pp. 457–481.CrossRefGoogle Scholar
  20. Shukla, M. B., Kok, R., Prasher, S. O., Clark, G., and Lacroix, R. (1996). “Use of artificial neural network in transient drainage design.” Trans. ASAE, Vol. 39, No. 1, pp. 119–124.CrossRefGoogle Scholar
  21. Singh, K. P., Basant, A., Malik, A., and Jain, G. (2009). “Artificial neural network modeling of the river water quality–A case study.” Ecol. Model., Vol. 220, No. 6, pp. 888–895.CrossRefGoogle Scholar
  22. Sobedji, J. M., Van Es, H. M., and Huston, J. L. (2001). “N fate and transport under variable cropping history and fertilizer rate on loamy sand and clay loam soils: I. Calibration of the LEACHMN model.” Plant Soil, Vol. 299, No. 1, pp. 57–70.CrossRefGoogle Scholar
  23. Thirumalaian, K. and Deo, M. C. (1998). “River stage forecasting using artificial neural network.” J. Hydrol. Eng., Vol. 3, No. 1, pp. 26–32.CrossRefGoogle Scholar
  24. Trajkovic, S., Todorovic, B., and Standkovic, M. (2003). “Forecasting of reference evapotranspiration by artificial neural network.” J. Irrig. And Drain., ASCE, Vol. 129, No. 6, pp. 454–457.CrossRefGoogle Scholar
  25. Tuppad, P., Douglas-Mankin, K. R., Lee, T., Srinivasan, R., and Arnold, J. G. (2011). “Soil and Water Assessment Tool (SWAT) hydrologic/water quality model: Extended capability and wider adoption.” Am. Soc. Agric. Biol. Eng., Vol. 54, No. 5, pp. 1677–1684.Google Scholar
  26. Wen, C. W. and Lee, C. S. (1998). “A neural networkapproach to multiobjective optimization for water quality management in a river basin.” Water Resour. Res., Vol. 34, No. 3, pp. 427–436.CrossRefGoogle Scholar
  27. Yang, C. C., Prasher, S. O., and Lacroix, R. (1996). “Application of artificial neural network to land drainage engineering.” Trans. ASAE, Vol. 39, No. 2, pp. 525–533.CrossRefGoogle Scholar
  28. Yang, C. C., Prasher, S. O., Lacroix, R., Sreekanth, S., Patni, N. K., and Masse, L. (1997). “Artificial neural network model for subsurfacedrained farmlands.” J. Irrig. And Drain., ASCE, Vol. 123, No. 4, pp. 285–292.CrossRefGoogle Scholar
  29. Zealand, C. M., Burn, D. H., and Simonovic, S. P. (1999). “Short term streamflow forecasting using artificial neural networks.” J. Hydrol., Vol. 214, No. 1–3, pp. 32–48.CrossRefGoogle Scholar
  30. Zhang, X., Xu, Z., Sun, X., Dong, W., and Ballantine, D. (2013). “Nitrate in shallow groundwater in typical agricultural and forest ecosystems in China, 2004-2010.” J. Environ Sci. (China), Vol. 25, No. 5, pp. 1007–1014.CrossRefGoogle Scholar

Copyright information

© Korean Society of Civil Engineers and Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  • Kaveh Ostad-Ali-Askari
    • 1
    Email author
  • Mohammad Shayannejad
    • 2
  • Hossein Ghorbanizadeh-Kharazi
    • 3
  1. 1.Dept. of Civil Engineering, Isfahan (Khorasgan) BranchIslamic Azad UniversityIsfahanIran
  2. 2.Water Engineering Dept.Isfahan University of TechnologyIsfahan, Isfahan ProvinceIran
  3. 3.Water Engineering Dept., Shoushtar BranchIslamic Azad UniversityShoushtar, Khuzestan ProvinceIran

Personalised recommendations