Research Article
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Year 2020, Volume 9, Issue 4, 339 - 348, 01.10.2020
https://doi.org/10.18393/ejss.783470

Abstract

References

  • Alavi, N., Nozari, V., Mazloumzadeh, S.M., Nezamabadi-pour, H., 2010. Irrigation Water quality evaluation using adaptive network-based fuzzy inference system. Paddy and Water Environment 8: 259-266.
  • Cemek, B., Ünlükara, A., Kurunç, A., Küçüktopcu, E., 2020. Leaf area modeling of bell pepper (Capsicum annuum L.) grown under different stress conditions by soft computing approaches. Computers and Electronics in Agriculture 174: 105514.
  • Eaton, F.M., 1950. Significance of carbonates in irrigation waters. Soil Science 69(2): 123–134.
  • Küçüktopcu, E., Cemek, B., 2020. The use of artificial neural networks to estimate optimum insulation thickness, energy savings, and carbon dioxide emissions. Environmental Progress & Sustainable Energy e13478.
  • Laluraj, C.M., Gopinath, G., 2006. Assessment on seasonal variation of groundwater quality of Phreatic aquifers – A river basin system. Environmental Monitoring and Assessment 117: 45-57.
  • McKone, T.E., Deshpande, A.W., 2005. Can fuzzy logic bring complex problems into focus? Modeling Imprecise Factors in Environmental Policy. Journal of Environmental Science and Technology 39(2): 42A-47A.
  • McNeil, F.M., Thro, E., 1994. Fuzzy Logic: A Practical Approach. AcademicPress, Boston MA, USA. 350p.
  • Mirabbasi, R., Mazloumzadeh, S.M., Rahnama, M.B., 2008. Evaluation of irrigation water quality using fuzzy logic. Research Journal of Environmental Sciences 2(5): 340-352.
  • Priya, K.L., Arulraj, G.P., 2011. A correlation-regression model for the physicochemical parameters of the groundwater in Coimbatore city, India. Environmental Technology 32(7): 731-738.
  • Richard, L.A., 1954. Diagnosis and improvement of saline and alkali soils. Agriculture Handbook Vol. 60. United States Department of Agriculture, Washington, DC, USA. 160p.
  • Taylor, K.E., 2001. Summarizing multiple aspects of model performance in a single diagram. Journal of Geophysical Research: Atmospheres 106(D7): 7183-7192.
  • Vadiati, M., Nalley, D., Adamowski, J., Nakhaei, M., Asghari-Moghaddam, A., 2019. A comparative study of fuzzy logic-based models for groundwater quality evaluation based on irrigation indices. Journal of Water and Land Development 43(1): 158-170.
  • Wilcox, L.V., 1955. Classification and use of irrigation waters. Circ. No. 969. US Dept Agriculture, Washington DC, 19p. Available at [Access date: 25.05.2019]: https://www.ars.usda.gov/arsuserfiles/20360500/pdf_pubs/P0192.pdf

Evaluation of quality of groundwater in irrigation using fuzzy logic in the Bafra Plain, Northern Turkey

Year 2020, Volume 9, Issue 4, 339 - 348, 01.10.2020
https://doi.org/10.18393/ejss.783470

Abstract

The quality of groundwater plays an important role in irrigation management and planning. The most commonly used method when classifying the irrigation water quality is the United States Soil Laboratory (USSL) diagram. Fuzzy logic approach is one of the widely used methods produced more precise and accurate results according to the USSL diagram. A rule-based, fuzzy logic irrigation water quality (FL-IWQ) were evaluated by using electrical conductivity (EC), sodium adsorption ratio (SAR), and residual sodium carbonate (RSC) values of groundwater in irrigation, in Bafra plain. The FL-IWQ defuzzification methods-center of area (COA), mean of maxima (MOM), least of maxima (LOM), and (SOM) were selected and compared with quality values of groundwater in irrigation. Based on the results of the FL-IWQ defuzzification methods with quality values of groundwater in irrigation, the determination of coefficients for COA, MOM, SOM and LOM were 0.9874, 0.9755, 0,9574 and 0.9453, respectively. Results obtained from FL-IWQ revealed that there has been 93% general agreement with the results obtained from the USSL diagram and RSC classification. The developed fuzzy model produced more reliable results for groundwater in irrigation than that of the USSL-diagram and RSC classification. The study suggests using proposed fuzzy model as a promising alternative to the traditional ones for classifying the quality of groundwater in irrigation under uncertain conditions.

References

  • Alavi, N., Nozari, V., Mazloumzadeh, S.M., Nezamabadi-pour, H., 2010. Irrigation Water quality evaluation using adaptive network-based fuzzy inference system. Paddy and Water Environment 8: 259-266.
  • Cemek, B., Ünlükara, A., Kurunç, A., Küçüktopcu, E., 2020. Leaf area modeling of bell pepper (Capsicum annuum L.) grown under different stress conditions by soft computing approaches. Computers and Electronics in Agriculture 174: 105514.
  • Eaton, F.M., 1950. Significance of carbonates in irrigation waters. Soil Science 69(2): 123–134.
  • Küçüktopcu, E., Cemek, B., 2020. The use of artificial neural networks to estimate optimum insulation thickness, energy savings, and carbon dioxide emissions. Environmental Progress & Sustainable Energy e13478.
  • Laluraj, C.M., Gopinath, G., 2006. Assessment on seasonal variation of groundwater quality of Phreatic aquifers – A river basin system. Environmental Monitoring and Assessment 117: 45-57.
  • McKone, T.E., Deshpande, A.W., 2005. Can fuzzy logic bring complex problems into focus? Modeling Imprecise Factors in Environmental Policy. Journal of Environmental Science and Technology 39(2): 42A-47A.
  • McNeil, F.M., Thro, E., 1994. Fuzzy Logic: A Practical Approach. AcademicPress, Boston MA, USA. 350p.
  • Mirabbasi, R., Mazloumzadeh, S.M., Rahnama, M.B., 2008. Evaluation of irrigation water quality using fuzzy logic. Research Journal of Environmental Sciences 2(5): 340-352.
  • Priya, K.L., Arulraj, G.P., 2011. A correlation-regression model for the physicochemical parameters of the groundwater in Coimbatore city, India. Environmental Technology 32(7): 731-738.
  • Richard, L.A., 1954. Diagnosis and improvement of saline and alkali soils. Agriculture Handbook Vol. 60. United States Department of Agriculture, Washington, DC, USA. 160p.
  • Taylor, K.E., 2001. Summarizing multiple aspects of model performance in a single diagram. Journal of Geophysical Research: Atmospheres 106(D7): 7183-7192.
  • Vadiati, M., Nalley, D., Adamowski, J., Nakhaei, M., Asghari-Moghaddam, A., 2019. A comparative study of fuzzy logic-based models for groundwater quality evaluation based on irrigation indices. Journal of Water and Land Development 43(1): 158-170.
  • Wilcox, L.V., 1955. Classification and use of irrigation waters. Circ. No. 969. US Dept Agriculture, Washington DC, 19p. Available at [Access date: 25.05.2019]: https://www.ars.usda.gov/arsuserfiles/20360500/pdf_pubs/P0192.pdf

Details

Primary Language English
Subjects Science
Journal Section Articles
Authors

Bilal CEMEK This is me (Primary Author)
Ondokuz Mayıs University, Faculty of Agriculture, Department of Agricultural Structures and Irrigation, Samsun Turkey
0000-0002-0503-6497
Türkiye


Recai OKTAŞ This is me
Ondokuz Mayıs University, Faculty of Engineering, Department of Computer Engineering, Samsun, Turkey
0000-0003-3282-3549
Türkiye

Publication Date October 1, 2020
Published in Issue Year 2020, Volume 9, Issue 4

Cite

Bibtex @research article { ejss783470, journal = {Eurasian Journal of Soil Science}, eissn = {2147-4249}, address = {}, publisher = {Avrasya Toprak Bilimleri Dernekleri Federasyonu}, year = {2020}, volume = {9}, number = {4}, pages = {339 - 348}, doi = {10.18393/ejss.783470}, title = {Evaluation of quality of groundwater in irrigation using fuzzy logic in the Bafra Plain, Northern Turkey}, key = {cite}, author = {Cemek, Bilal and Oktaş, Recai} }
APA Cemek, B. & Oktaş, R. (2020). Evaluation of quality of groundwater in irrigation using fuzzy logic in the Bafra Plain, Northern Turkey . Eurasian Journal of Soil Science , 9 (4) , 339-348 . DOI: 10.18393/ejss.783470
MLA Cemek, B. , Oktaş, R. "Evaluation of quality of groundwater in irrigation using fuzzy logic in the Bafra Plain, Northern Turkey" . Eurasian Journal of Soil Science 9 (2020 ): 339-348 <https://dergipark.org.tr/en/pub/ejss/issue/54966/783470>
Chicago Cemek, B. , Oktaş, R. "Evaluation of quality of groundwater in irrigation using fuzzy logic in the Bafra Plain, Northern Turkey". Eurasian Journal of Soil Science 9 (2020 ): 339-348
RIS TY - JOUR T1 - Evaluation of quality of groundwater in irrigation using fuzzy logic in the Bafra Plain, Northern Turkey AU - BilalCemek, RecaiOktaş Y1 - 2020 PY - 2020 N1 - doi: 10.18393/ejss.783470 DO - 10.18393/ejss.783470 T2 - Eurasian Journal of Soil Science JF - Journal JO - JOR SP - 339 EP - 348 VL - 9 IS - 4 SN - -2147-4249 M3 - doi: 10.18393/ejss.783470 UR - https://doi.org/10.18393/ejss.783470 Y2 - 2020 ER -
EndNote %0 Eurasian Journal of Soil Science Evaluation of quality of groundwater in irrigation using fuzzy logic in the Bafra Plain, Northern Turkey %A Bilal Cemek , Recai Oktaş %T Evaluation of quality of groundwater in irrigation using fuzzy logic in the Bafra Plain, Northern Turkey %D 2020 %J Eurasian Journal of Soil Science %P -2147-4249 %V 9 %N 4 %R doi: 10.18393/ejss.783470 %U 10.18393/ejss.783470
ISNAD Cemek, Bilal , Oktaş, Recai . "Evaluation of quality of groundwater in irrigation using fuzzy logic in the Bafra Plain, Northern Turkey". Eurasian Journal of Soil Science 9 / 4 (October 2020): 339-348 . https://doi.org/10.18393/ejss.783470
AMA Cemek B. , Oktaş R. Evaluation of quality of groundwater in irrigation using fuzzy logic in the Bafra Plain, Northern Turkey. EJSS. 2020; 9(4): 339-348.
Vancouver Cemek B. , Oktaş R. Evaluation of quality of groundwater in irrigation using fuzzy logic in the Bafra Plain, Northern Turkey. Eurasian Journal of Soil Science. 2020; 9(4): 339-348.
IEEE B. Cemek and R. Oktaş , "Evaluation of quality of groundwater in irrigation using fuzzy logic in the Bafra Plain, Northern Turkey", Eurasian Journal of Soil Science, vol. 9, no. 4, pp. 339-348, Oct. 2020, doi:10.18393/ejss.783470