Environmental Monitoring and Assessment

, Volume 118, Issue 1–3, pp 337–354 | Cite as

A Fuzzy Logic Approach to Assess Groundwater Pollution Levels Below Agricultural Fields

  • Ayse Muhammetoglu
  • Ahmet Yardimci


A fuzzy logic approach has been developed to assess the groundwater pollution levels below agricultural fields. The data collected for Kumluca Plain of Turkey have been utilized to develop the approach. The plain is known with its intensive agricultural activities, which imply excessive application of fertilizers. The characteristics of the soils and underlying groundwater for this plain were monitored during the years 1999 and 2000. Additionally, an extensive field survey related to the types and yields of crops, fertilizer application and irrigation water was carried out. Both the soil and groundwater have exhibited high levels of nitrogen, phosphorus and salinity with considerable spatial and temporal variations. The pollution level of groundwater at several established stations within the plain were assessed using Fuzzy Logic. Water Pollution Index (WPI) values are calculated by Fuzzy Logic utilizing the most significant groundwater pollutants in the area namely nitrite, nitrate and orthophosphate together with the groundwater vulnerability to pollution. The results of the calculated WPI and the monitoring study have yielded good agreement. WPI indicated high to moderate water pollution levels at Kumluca plain depending on factors such as agricultural age, depth to groundwater, soil characteristics and vulnerability of groundwater to pollution. Fuzzy Logic approach has shown to be a practical, simple and useful tool to assess groundwater pollution levels.


agriculture fertilizers Fuzzy Logic groundwater nitrate nitrite orthophosphate pollution Seepage Index Number Water Pollution Index 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Boers, P. C. M.: 1996, ‘Nutrient emissions from agriculture in the Netherlands, Causes and Remedies’ Water Sci. Technol. 33(4–5), 183–189.CrossRefGoogle Scholar
  2. Burkart, M. R. and Feher, J.: 1996, ‘Regional estimation of groundwater vulnerability to non-point sources of agricultural chemicals’, Water Sci. Technol. 33(4/5), 241–247.CrossRefGoogle Scholar
  3. Burkart, M. R. and Stener, J. D.: 2002, ‘Nitrate in aquifers beneath agricultural systems’, Water Sci. Technol. 45(9), 19–29.Google Scholar
  4. Chang, N., Chen, H. W. and Ning, S. K.: 2001, ‘Identification of river water quality using the Fuzzy synthetic evaluation approach’, J. Environ. Manage. 63, 293–305.CrossRefGoogle Scholar
  5. Cook, M. G., Hunt, P. G., Stone, K. C. and Canterberry, J. H.: 1996, ‘Reducing diffuse pollution through implementation of agricultural best management practices: A case study’, Water Sci. Technol. 33(4/5), 191–196.CrossRefGoogle Scholar
  6. Dahab, M. F., Lee, Y. W. and Bogardi, I.: 1994, ‘A rule based fuzzy-set approach to risk analysis of nitrate contaminated groundwater’, Water Sci. Technol. 30(7), 45–52.Google Scholar
  7. Dixon, B.: 2004, ‘Prediction of groundwater vulnerability using an Integrated GIS based neuro-fuzzy techniques’, J. Spatial Hydrol. 4(2), 38.Google Scholar
  8. Dixon, B.: 2005, ‘Application of neuro-fuzzy techniques in predicting groundwater vulnerability: A GIS based sensitivity analysis’, J. Hydrol. 309(1–4), 17–38.CrossRefGoogle Scholar
  9. Dixon, B., Scott, H. D., Dixon, J. C. and Steele, K. F.: 2002, ‘Prediction of aquifer vulnerability to pesticides using Fuzzy-Rule based models of the regional scale’, Phys. Geogr. 23, 130–152.Google Scholar
  10. Dojlido, J., Raniszeski, J. and Woyciechowska, J.: 1994, ‘Water quality index-application for rivers in Vistula river basin in Poland’, Water Sci. Technol. 30(10), 57–64.Google Scholar
  11. Heinonen, P. and Herve, S.: 1994, ‘The development of a new water quality classification system for Finland’, Water Sci. Technol. 30(10), 21–24.Google Scholar
  12. Heinz, I., Brouwer, F. and Zabel, T.: 2002, ‘Interrelationships between voluntary approaches and mandatory regulations in the EU to control diffuse water pollutions caused by agriculture’, Proceedings of IWA 6th International Conf. on Diffuse Pollution, Amsterdam, 30 Sept.–4 Oct. 2002, pp. 21–28.Google Scholar
  13. Ignazi, J. C.: 1993, ‘Improving nitrogen management in irrigated, intensely cultivated areas: The approach in France’, in: Prevention of Water Pollution by Agriculture and Related Activities. Proceedings of the FAO Expert Consultation, Santiago, Chile, 20–23 Oct. 1992. Water Report 1. FAO, Rome, pp. 247–261.Google Scholar
  14. IHE, Hydroinformatics: 2000, ‘Use of Artificial Neural Network and Fuzzy Logic for Integrated Water Management: Review of Applications’, Project Report, Delft.Google Scholar
  15. Jamshidi, M.: 2003, ‘Tools for Intelligent Control: Fuzzy Controllers, Neural Networks and Genetic Algorithms’, Phil. Trans. R. Soc. 361, 1781–1808.CrossRefGoogle Scholar
  16. Meinardi, C. R., Beusen, A. H. W., Bollen, M. J. S., Klepper, O. and Williams, W. J.: 1995, ‘Vulnerability to diffuse pollution and average nitrate contamination of European soils and groundwater’, Water Sci. Technol. 31(8), 159–165.CrossRefGoogle Scholar
  17. Moore and John, S.: 1990, ‘SEEPAGE: A System for Early Evaluation of the Pollution Potential of Agricultural Groundwater Environments’, USDA. SCS, Northeast Technical Center, Geology Technical Note 5.Google Scholar
  18. Muhammetoglu, H., Muhammetoglu, A. and Soyupak, S.: 2002, ‘Vulnerability of groundwater to pollution from agricultural diffuse sources: A case study’, Water Sci. Technol. 45(9),1–7.Google Scholar
  19. Muhammetoglu, H., Soyupak, S. and Muhammetoglu, A.: 2003, ‘Investigation of Groundwater Pollution from Agricultural and Domestic Wastewater Using the Nitrogen Balance Approach’, The Scientific and Technical Research Council of Turkey, Project No. 198Y059, Final Report (in Turkish).Google Scholar
  20. Muhammetoglu, H., Muhammetoglu, A. and Soyupak, S.: 2005, ‘Assessment of nitrogen excess in an agricultural area using a nitrogen balance approach’, Water Sci. Technol. 51(3/4), 259–266.Google Scholar
  21. Navulur, K. C. S. and Engel, B. A.: 2005, ‘Predicting Spatial Distributions of Vulnerability of Indiana State Aquifer Systems to Nitrate Leaching using a GIS’, in
  22. Novotny, V.: 1999, ‘Diffuse pollution from agriculture-a world wide outlook’, Water Sci. Technol. 39(3), 1–13.CrossRefGoogle Scholar
  23. Novotny, V.: 2002, Water Quality: Diffuse Pollution and Watershed Management, J. Wiley and Sons, New York, NY.Google Scholar
  24. Novotny, V.: 2005, ‘The next step incorporating diffuse pollution abatement into watershed management’, Water Sci. Technol. 51(3–4), 1–9.Google Scholar
  25. Ott, W. R.: 1978, ‘Water Quality Indices: A Survey of Indices Used in the United States’, EPA-600/4-78-005, Washington, DC: US Environmental Protection Agency, 128 pp.Google Scholar
  26. Rondeau, L., Ruelos, R., Levrat, L. and Lamotte, M.: 1997, ‘A defuzzification method respecting the fuzzification’, Fuzzy Set Syst. 86, 311–320.CrossRefGoogle Scholar
  27. Silvert, W.: 2000, ‘Fuzzy indices of environmental conditions’, Ecol. Model 130, 111–119.CrossRefGoogle Scholar
  28. Suvarna, A. C. and Somashekar, R. K.: 1997, ‘Evaluation of water-quality index of river Cauvery and its tributaries’, Curr. Sci. 72, 640–646.Google Scholar
  29. Tchobanoglous, G., Burton, F. L. and Stensel, H. D.: 2002, Wastewater Engineering, Treatment, Disposal, Reuse, 4th edn., Metcalf & Eddy, Inc., McGraw-Hill Publishing Company Ltd.Google Scholar
  30. TĉV: 2002, Turkish Environmental Law, Published by Foundation of Turkish Environment, Vol. II.Google Scholar
  31. US EPA: 1994, ‘National Water Quality Inventory', 1992 Report to Congress. EPA-841-R-94-001. Office of Water, Washington, DC.Google Scholar
  32. Woldt, W., Dahab, M., Bogardi, I. and Dou, C.: 1996, ‘Management of diffuse pollution in groundwater under imprecise conditions using fuzzy models’, Water Sci. Technol. 33(4/5), 249–257.CrossRefGoogle Scholar
  33. Zrilic, D. G., Angulo, J. R. and Yuan, B.: 2000, ‘Hardware implementations of fuzzy membership functions, operations and inference’, Comput. Electr. Eng. 26, 85–105.CrossRefGoogle Scholar

Copyright information

© Springer Science + Business Media, Inc. 2006

Authors and Affiliations

  1. 1.Faculty of Engineering, Department of Environmental EngineeringAkdeniz UniversityAntalyaTurkey
  2. 2.Industrial Automation ProgramAkdeniz UniversityAntalyaTurkey

Personalised recommendations