Araştırma Makalesi
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Fuzzy Logic-Based Evaluation of Physicochemical Water Quality Parameters in the Gökırmak River (Türkiye)

Yıl 2025, Cilt: 56 Sayı: 3, 234 - 242, 26.09.2025
https://doi.org/10.17097/agricultureatauni.1693998

Öz

Traditional water quality classification methods rely on fixed threshold values, which limits their ability to reflect the degree of deviation from these boundaries. This rigid approach often results in uncertainties when assessing the ecological status of rivers. Fuzzy logic, in contrast, provides a more flexible framework by incorporating gradual transitions between classes and accounting for the relative importance of parameters. In this study, a fuzzy logic-based classification system was developed to evaluate the water quality of the Gökırmak River (Türkiye) and was compared with the conventional water quality index defined by national standards. Ten physicochemical parameters (temperature, pH, dissolved oxygen, electrical conductivity, nitrate, nitrite, ammonium, phosphate, biochemical oxygen demand, and chemical oxygen demand) were monitored monthly at six stations for one year. The fuzzy logic model was constructed using triangular membership functions and a Mamdani inference system. Model performance was assessed by comparing fuzzy classification results with expert evaluations based on the Surface Water Regulation. The system achieved 90% agreement, calculated as the ratio of consistent classifications to the total number of cases, demonstrating that fuzzy logic can serve as a reliable tool in water quality assessment. The findings highlight that fuzzy logic-based approaches not only reduce classification uncertainties but also provide a decision support framework for sustainable water resource management. Further research should expand the dataset across longer time periods and incorporate retrospective records to improve generalizability.

Destekleyen Kurum

Kastamonu University

Proje Numarası

KÜ-BAP01/2020-9

Teşekkür

The study was financially supported by the Kastamonu University, Coordination Unit of Scientific Research Projects (Project no: KÜ-BAP01/2020-9).

Kaynakça

  • Abdullah, M. P., Waseem, S., Bai, V. R., & Mohsin, I. (2008). Development of new water quality model using fuzzy logic system for Malaysia. Open Environmental Sciences, 2, 101-106. https://doi.org/10.2174/1876325 100802010101
  • Akkaptan, A. (2012). Hayvancılıkta bulanık mantık tabanlı karar destek sistemi (Master’s Thesis, Ege University). (In Turkish)
  • APHA. (2017). Standard methods for the examination of water and wastewater (23 ed.). APHA, AWWA, WEF.
  • Atea, E. A. H., Kadak, A. E., Yaganoglu, A. M., & Sonmez, A. Y. (2018). Fuzzy logic evaluation of water quality classification for some physicochemical parameters in Germectepe Dam Lake (Kastamonu-Turkey). Fresenius Environmental Bulletin, 27(8), 5238-5243.
  • Azzirgue, E. M., Cherif, E. K., Tchakoucht, T. A., Azhari, H. E., & Salmoun, F. (2022). Testing groundwater quality in Jouamaa Hakama Region (North of Morocco) using water quality indices (WQIs) and fuzzy logic method: An exploratory study. Water, 14(19), 3028. https://doi.org/ 10.3390/w14193028
  • Chanapathi, T., & Thatikonda, S. (2019). Fuzzy-based regional water quality index for surface water quality assessment. Journal of Hazardous, Toxic, and Radioactive Waste, 23(4), 04019010. https://doi.org/10.1061/(ASCE)HZ.215 3-5515.0000443
  • de Oliveira, M. D., de Rezende, O. L. T., Oliveira, S. M. A. C., & Libanio, M. (2014). A new approach to the raw water quality index. Engenharia Sanitaria e Ambiental, 19(4), 361-372. https://doi.org/10.1590/S1413-41522014019 000000803
  • Dengiz, O., Özyazici, M. A., & Sağlam, M. (2015). Multi-criteria assessment and geostatistical approach for determination of rice growing suitability sites in Gokirmak catchment. Paddy and Water Environment, 13(1), 1–10. https://doi.org/10.1007/s10333-013-0400-4
  • Dewanti, N. A., & Abadi, A. M. (2019). Fuzzy logic application as a tool for classifying water quality status in Gajahwong River, Yogyakarta, Indonesia. In IOP Conference Series: Materials Science and Engineering, 546(3), 032005.
  • Gharibi, H., Mahvi, A. H., Nabizadeh, R., Arabalibeik, H., Yunesian, M., & Sowlat, M. H. (2012). A Novel approach in water quality assessment based on fuzzy logic. Journal of Environmental Management, 112, 87-95. https://doi.org/10.1016/j.jenvman.2012.07.007
  • Icaga, Y. (2007). Fuzzy evaluation of water quality classification. Ecological Indicators, 7(3), 710-718. https://doi.org/10.1016/j.ecolind.2006.08.002
  • Kale, S. (2020). Development of an adaptive neuro-fuzzy inference system (ANFIS) model to predict sea surface temperature (SST). Oceanological and Hydrobiological Studies, 49(4), 354-373. https://doi.org/10.1515/ohs202 0-0031
  • Kale, S. (2024). Fuzzy logic approaches to water quality assessment In Ö. Aksu (Ed.), International Studies and Evaluations in the Field of Aquaculture Sciences (pp. 53-65). Serüven Publishing.
  • Liou, S. M., Lo, S. L., & Hu, C. Y. (2003). Application of two-stage fuzzy set theory to river quality evaluation in Taiwan. Water Research, 37(6), 1406-1416. https://doi.org/10.1016/S0043-1354(02)00479-7 Liou, Y. T., & Lo, S. L. (2005). A fuzzy index model for trophic status evaluation of reservoir waters. Water Research, 39, 1415-1423. https://doi.org/10.1016/j.watres.2005.0 1.014
  • Ludwig, B., & Tulbure, I. (1996). Contributions to an aggregated environmental pollution index. In: Proceedings of the Intersociety Energy Conversion Engineering Conference, 3, 2144-2149.
  • Moradi, S., Raeisi, N., Mehta, D., & Eslamian, S. (2025). Investigation of the effect of water treatment plant effluent on river quality: A case study. International Journal of Environment and Waste Management, 36(4), 438–451. https://doi.org/10.1504/IJEWM.2023.100563 74
  • Ocampo-Duque, W., Ferre-Huguet, N., Domingo, J. L., & Schuhmacher, M. (2006). Assessing water quality in rivers with fuzzy inference systems: A case study. Environment International, 32, 733-742. https://doi.org/10.1016/j.envint.2006.03.009
  • Oladipo, J. O., Akinwumiju, A. S., Aboyeji, O. S., & Adelodun, A. A. (2021). Comparison between fuzzy logic and water quality index methods: A case of water quality assessment in Ikare Community, Southwestern Nigeria. Environmental Challenges, 3, 100038. https://doi.org/10.1016/j.envc.2021.100038
  • Raman, B. V., Bouwmeester, R., & Mohan, S. (2009). Fuzzy logic water quality index and importance of water quality parameters. Air, Soil and Water Research, 2, 51-59. https://doi.org/10.4137/ASWR.S2156
  • Ross, T. J. (2004). Fuzzy logic with engineering applications. John Wiley & Sons Ltd.
  • Sadiq, R., & Rodriguez, M. J. (2004). Fuzzy synthetic evaluation of disinfection by-products: A risk-based indexing system. Journal of Environmental Management, 73, 1-13. https://doi.org/10.1016/j.jenvman.2004.04.014
  • Said, A., Stevens, D., & Selke, G. (2004). An innovative index for evaluating water quality in streams. Environmental Management, 34, 406-414. https://doi.org/10.1007/s0 0267-004-0210-y
  • Scannapieco, D., Naddeo, V., Zarra, T., & Belgiorno, V. (2012). River water quality assessment: A comparison of binary- and fuzzy logic-based approaches. Ecological Engineering, 47, 132-140. https://doi.org/10.1016/j. ecoleng.2012.06.015
  • Shen, G., Lu, Y., Wang, M., & Sun, Y. (2005). Status and fuzzy comprehensive assessment of combined heavy metal and organochlorine pesticide pollution in Taihu Lake region of China. Journal of Environmental Management, 76, 355-362. https://doi.org/10.1016/j.jenvman.2005.0 2.011
  • Sivanandam, S. N., Sumathi, S., & Deepa, S.N. (2007). Introduction to fuzzy logic using MATLAB. Springer.
  • Sönmez, A. Y., Hisar, O., & Yanık, T. (2013). A comparative analysis of water quality assessment methods for heavy metal pollution in Karasu Stream, Turkey. Fresenius Environmental Bulletin, 22(2A), 579-583.
  • Sönmez, A. Y., Kale, S., Ozdemir, R. C., & Kadak, A. E. (2018). An adaptive neuro-fuzzy inference system (ANFIS) to predict of cadmium (Cd) concentrations in the Filyos River, Turkey. Turkish Journal of Fisheries and Aquatic Sciences, 18(12), 1333-1343. https://doi.org/10.419 4/1303-2712-v18_12_01
  • Sönmez, A. Y., & Taştan, Y. (2024). Use of fuzzy logic in water quality classification. In Ö. Aksu (Ed.), International Studies and Evaluations in the Field of Aquaculture Sciences (pp. 17-30). Serüven Publishing.
  • Sönmez, A. Y. (2011). Karasu Irmağında ağır metal kirliliğinin belirlenmesi ve bulanık mantıkla değerlendirilmesi (Doctoral dissertation, Atatürk University). (In Turkish)
  • Tanatmış, M. (2004). The Ephemeroptera (Insecta) fauna of the Gökırmak river basin (Kastamonu) and of the seashore lying between Cide (Kastamonu)-Ayancık (Sinop). Türk Entomoloji Dergisi, 28(1), 45-56.
  • Trach, R., Trach, Y., Kiersnowska, A., Markiewicz, A., Lendo-Siwicka, M., & Rusakov, K. (2022). A study of assessment and prediction of water quality index using fuzzy logic and ANN models. Sustainability, 14(9), 5656. https://doi.org/10.3390/su14095656
  • Wang, L. J., & Zou, Z. H. (2008). Application of improved attributes recognition method in water quality assessment. Chinese Journal of Environmental Engineering, 2, 553-556.
  • Yildirim, C., Schildgen, T. F., Echtler, H., Melnick, D., Bookhagen, B., Ciner, A., Niedermann, S., Merhel, S., Martschini, M., Steier, P., & Strecker, M. R. (2013). Tectonic implications of fluvial incision and pediment deformation at the northern margin of the Central Anatolian Plateau based on multiple cosmogenic nuclides. Tectonics, 32(5), 1107–1120. https://doi.org/10.1002/tect.20066
  • YSY. (2012). Yüzey Suları Yönetmeliği. Ministry of Forestry and Water Managements, Turkish Republic Official Gazette, publication date 15.04.2015, Number: 29327, Ankara. (In Turkish)
  • Zadeh, L. A. (1988). Fuzzy logic. Computer, 21(4), 83-93. https://doi.org/10.1109/2.53

Gökırmak Nehri'nin (Türkiye) Fizikokimyasal Su Kalitesinin Bulanık Mantık ile Değerlendirilmesi

Yıl 2025, Cilt: 56 Sayı: 3, 234 - 242, 26.09.2025
https://doi.org/10.17097/agricultureatauni.1693998

Öz

Geleneksel su kalitesi sınıflandırma yöntemleri sabit eşik değerlerine dayanmaktadır ve bu durum ölçüm değerlerinin bu sınırların ne kadar uzağında veya yakınında olduğunu yansıtamamaktadır. Bu katı yaklaşım, nehirlerin ekolojik durumunun değerlendirilmesinde belirsizliklere yol açmaktadır. Buna karşılık, bulanık mantık, sınıflar arasında kademeli geçişlere izin vererek ve parametrelerin göreli önemini dikkate alarak daha esnek bir değerlendirme çerçevesi sunmaktadır. Bu çalışmada, Türkiye’deki Gökırmak Nehri’nin su kalitesini değerlendirmek amacıyla bulanık mantık tabanlı bir sınıflandırma sistemi geliştirilmiş ve ulusal standartlarda tanımlanan klasik su kalite indeksi ile karşılaştırılmıştır. On farklı fizikokimyasal parametre (sıcaklık, pH, çözünmüş oksijen, elektriksel iletkenlik, nitrat, nitrit, amonyum, fosfat, biyokimyasal oksijen ihtiyacı ve kimyasal oksijen ihtiyacı) bir yıl boyunca altı istasyonda aylık olarak izlenmiştir. Bulanık mantık modeli üçgensel üyelik fonksiyonları ve Mamdani çıkarım sistemi ile oluşturulmuştur. Modelin performansı, Yüzey Suları Yönetmeliği’ne göre uzman değerlendirmeleri ile karşılaştırılarak test edilmiştir. Sistem, toplam sınıflandırmaların %90’ında uyum sağlamış olup bu oran, bulanık mantık yaklaşımının su kalitesi değerlendirmesinde güvenilir bir araç olduğunu göstermektedir. Bulgular, bulanık mantık tabanlı yöntemlerin sınıflandırma belirsizliklerini azaltmanın yanı sıra sürdürülebilir su kaynakları yönetimi için karar destek aracı sağlayabileceğini ortaya koymaktadır. Daha geniş veri setlerinin ve geçmiş yıllara ait kayıtların dahil edilmesi, yöntemin genellenebilirliğini artırmak için önerilmektedir.

Destekleyen Kurum

Kastamonu Üniversitesi

Proje Numarası

KÜ-BAP01/2020-9

Teşekkür

Çalışma Kastamonu Üniversitesi Bilimsel Araştırma Projeleri Koordinasyon Birimi tarafından finansal olarak desteklenmiştir (Proje no: KÜ-BAP01/2020-9).

Kaynakça

  • Abdullah, M. P., Waseem, S., Bai, V. R., & Mohsin, I. (2008). Development of new water quality model using fuzzy logic system for Malaysia. Open Environmental Sciences, 2, 101-106. https://doi.org/10.2174/1876325 100802010101
  • Akkaptan, A. (2012). Hayvancılıkta bulanık mantık tabanlı karar destek sistemi (Master’s Thesis, Ege University). (In Turkish)
  • APHA. (2017). Standard methods for the examination of water and wastewater (23 ed.). APHA, AWWA, WEF.
  • Atea, E. A. H., Kadak, A. E., Yaganoglu, A. M., & Sonmez, A. Y. (2018). Fuzzy logic evaluation of water quality classification for some physicochemical parameters in Germectepe Dam Lake (Kastamonu-Turkey). Fresenius Environmental Bulletin, 27(8), 5238-5243.
  • Azzirgue, E. M., Cherif, E. K., Tchakoucht, T. A., Azhari, H. E., & Salmoun, F. (2022). Testing groundwater quality in Jouamaa Hakama Region (North of Morocco) using water quality indices (WQIs) and fuzzy logic method: An exploratory study. Water, 14(19), 3028. https://doi.org/ 10.3390/w14193028
  • Chanapathi, T., & Thatikonda, S. (2019). Fuzzy-based regional water quality index for surface water quality assessment. Journal of Hazardous, Toxic, and Radioactive Waste, 23(4), 04019010. https://doi.org/10.1061/(ASCE)HZ.215 3-5515.0000443
  • de Oliveira, M. D., de Rezende, O. L. T., Oliveira, S. M. A. C., & Libanio, M. (2014). A new approach to the raw water quality index. Engenharia Sanitaria e Ambiental, 19(4), 361-372. https://doi.org/10.1590/S1413-41522014019 000000803
  • Dengiz, O., Özyazici, M. A., & Sağlam, M. (2015). Multi-criteria assessment and geostatistical approach for determination of rice growing suitability sites in Gokirmak catchment. Paddy and Water Environment, 13(1), 1–10. https://doi.org/10.1007/s10333-013-0400-4
  • Dewanti, N. A., & Abadi, A. M. (2019). Fuzzy logic application as a tool for classifying water quality status in Gajahwong River, Yogyakarta, Indonesia. In IOP Conference Series: Materials Science and Engineering, 546(3), 032005.
  • Gharibi, H., Mahvi, A. H., Nabizadeh, R., Arabalibeik, H., Yunesian, M., & Sowlat, M. H. (2012). A Novel approach in water quality assessment based on fuzzy logic. Journal of Environmental Management, 112, 87-95. https://doi.org/10.1016/j.jenvman.2012.07.007
  • Icaga, Y. (2007). Fuzzy evaluation of water quality classification. Ecological Indicators, 7(3), 710-718. https://doi.org/10.1016/j.ecolind.2006.08.002
  • Kale, S. (2020). Development of an adaptive neuro-fuzzy inference system (ANFIS) model to predict sea surface temperature (SST). Oceanological and Hydrobiological Studies, 49(4), 354-373. https://doi.org/10.1515/ohs202 0-0031
  • Kale, S. (2024). Fuzzy logic approaches to water quality assessment In Ö. Aksu (Ed.), International Studies and Evaluations in the Field of Aquaculture Sciences (pp. 53-65). Serüven Publishing.
  • Liou, S. M., Lo, S. L., & Hu, C. Y. (2003). Application of two-stage fuzzy set theory to river quality evaluation in Taiwan. Water Research, 37(6), 1406-1416. https://doi.org/10.1016/S0043-1354(02)00479-7 Liou, Y. T., & Lo, S. L. (2005). A fuzzy index model for trophic status evaluation of reservoir waters. Water Research, 39, 1415-1423. https://doi.org/10.1016/j.watres.2005.0 1.014
  • Ludwig, B., & Tulbure, I. (1996). Contributions to an aggregated environmental pollution index. In: Proceedings of the Intersociety Energy Conversion Engineering Conference, 3, 2144-2149.
  • Moradi, S., Raeisi, N., Mehta, D., & Eslamian, S. (2025). Investigation of the effect of water treatment plant effluent on river quality: A case study. International Journal of Environment and Waste Management, 36(4), 438–451. https://doi.org/10.1504/IJEWM.2023.100563 74
  • Ocampo-Duque, W., Ferre-Huguet, N., Domingo, J. L., & Schuhmacher, M. (2006). Assessing water quality in rivers with fuzzy inference systems: A case study. Environment International, 32, 733-742. https://doi.org/10.1016/j.envint.2006.03.009
  • Oladipo, J. O., Akinwumiju, A. S., Aboyeji, O. S., & Adelodun, A. A. (2021). Comparison between fuzzy logic and water quality index methods: A case of water quality assessment in Ikare Community, Southwestern Nigeria. Environmental Challenges, 3, 100038. https://doi.org/10.1016/j.envc.2021.100038
  • Raman, B. V., Bouwmeester, R., & Mohan, S. (2009). Fuzzy logic water quality index and importance of water quality parameters. Air, Soil and Water Research, 2, 51-59. https://doi.org/10.4137/ASWR.S2156
  • Ross, T. J. (2004). Fuzzy logic with engineering applications. John Wiley & Sons Ltd.
  • Sadiq, R., & Rodriguez, M. J. (2004). Fuzzy synthetic evaluation of disinfection by-products: A risk-based indexing system. Journal of Environmental Management, 73, 1-13. https://doi.org/10.1016/j.jenvman.2004.04.014
  • Said, A., Stevens, D., & Selke, G. (2004). An innovative index for evaluating water quality in streams. Environmental Management, 34, 406-414. https://doi.org/10.1007/s0 0267-004-0210-y
  • Scannapieco, D., Naddeo, V., Zarra, T., & Belgiorno, V. (2012). River water quality assessment: A comparison of binary- and fuzzy logic-based approaches. Ecological Engineering, 47, 132-140. https://doi.org/10.1016/j. ecoleng.2012.06.015
  • Shen, G., Lu, Y., Wang, M., & Sun, Y. (2005). Status and fuzzy comprehensive assessment of combined heavy metal and organochlorine pesticide pollution in Taihu Lake region of China. Journal of Environmental Management, 76, 355-362. https://doi.org/10.1016/j.jenvman.2005.0 2.011
  • Sivanandam, S. N., Sumathi, S., & Deepa, S.N. (2007). Introduction to fuzzy logic using MATLAB. Springer.
  • Sönmez, A. Y., Hisar, O., & Yanık, T. (2013). A comparative analysis of water quality assessment methods for heavy metal pollution in Karasu Stream, Turkey. Fresenius Environmental Bulletin, 22(2A), 579-583.
  • Sönmez, A. Y., Kale, S., Ozdemir, R. C., & Kadak, A. E. (2018). An adaptive neuro-fuzzy inference system (ANFIS) to predict of cadmium (Cd) concentrations in the Filyos River, Turkey. Turkish Journal of Fisheries and Aquatic Sciences, 18(12), 1333-1343. https://doi.org/10.419 4/1303-2712-v18_12_01
  • Sönmez, A. Y., & Taştan, Y. (2024). Use of fuzzy logic in water quality classification. In Ö. Aksu (Ed.), International Studies and Evaluations in the Field of Aquaculture Sciences (pp. 17-30). Serüven Publishing.
  • Sönmez, A. Y. (2011). Karasu Irmağında ağır metal kirliliğinin belirlenmesi ve bulanık mantıkla değerlendirilmesi (Doctoral dissertation, Atatürk University). (In Turkish)
  • Tanatmış, M. (2004). The Ephemeroptera (Insecta) fauna of the Gökırmak river basin (Kastamonu) and of the seashore lying between Cide (Kastamonu)-Ayancık (Sinop). Türk Entomoloji Dergisi, 28(1), 45-56.
  • Trach, R., Trach, Y., Kiersnowska, A., Markiewicz, A., Lendo-Siwicka, M., & Rusakov, K. (2022). A study of assessment and prediction of water quality index using fuzzy logic and ANN models. Sustainability, 14(9), 5656. https://doi.org/10.3390/su14095656
  • Wang, L. J., & Zou, Z. H. (2008). Application of improved attributes recognition method in water quality assessment. Chinese Journal of Environmental Engineering, 2, 553-556.
  • Yildirim, C., Schildgen, T. F., Echtler, H., Melnick, D., Bookhagen, B., Ciner, A., Niedermann, S., Merhel, S., Martschini, M., Steier, P., & Strecker, M. R. (2013). Tectonic implications of fluvial incision and pediment deformation at the northern margin of the Central Anatolian Plateau based on multiple cosmogenic nuclides. Tectonics, 32(5), 1107–1120. https://doi.org/10.1002/tect.20066
  • YSY. (2012). Yüzey Suları Yönetmeliği. Ministry of Forestry and Water Managements, Turkish Republic Official Gazette, publication date 15.04.2015, Number: 29327, Ankara. (In Turkish)
  • Zadeh, L. A. (1988). Fuzzy logic. Computer, 21(4), 83-93. https://doi.org/10.1109/2.53
Toplam 35 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Sulama Suyu Kalitesi
Bölüm Araştırma Makalesi
Yazarlar

Adem Yavuz Sönmez 0000-0002-7043-1987

Semih Kale 0000-0001-5705-6935

Yiğit Taştan 0000-0002-6782-1597

Rahmi Can Özdemir 0000-0001-9986-0868

Ali Eslem Kadak 0000-0002-7128-9134

Proje Numarası KÜ-BAP01/2020-9
Yayımlanma Tarihi 26 Eylül 2025
Gönderilme Tarihi 7 Mayıs 2025
Kabul Tarihi 16 Eylül 2025
Yayımlandığı Sayı Yıl 2025 Cilt: 56 Sayı: 3

Kaynak Göster

APA Sönmez, A. Y., Kale, S., Taştan, Y., … Özdemir, R. C. (2025). Fuzzy Logic-Based Evaluation of Physicochemical Water Quality Parameters in the Gökırmak River (Türkiye). Research in Agricultural Sciences, 56(3), 234-242. https://doi.org/10.17097/agricultureatauni.1693998

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