Yıl: 2018 Cilt: 18 Sayı: 12 Sayfa Aralığı: 1333 - 1343 Metin Dili: İngilizce DOI: 10.4194/1303-2712-v18_12_01 İndeks Tarihi: 14-07-2020

An Adaptive Neuro-Fuzzy Inference System (ANFIS) to Predict of Cadmium (Cd) Concentrations in the Filyos River, Turkey

Öz:
Water quality is one of the main characteristics of a river system and prediction of water quality is the key factor inwater resource management. Different physical, biological and chemical parameters including heavy metals can be used toassess river water quality. Evaluation of the water quality in the rivers is quite difficult and requires more time and effortbecause of the fact that many factors affect water quality. Traditional data processing methods are insufficient to solve thisproblem. Therefore, in this study, an adaptive neuro-fuzzy inference system (ANFIS) model was developed to predict theconcentrations of cadmium (Cd) in the Filyos River, Turkey. For this purpose, water samples collected at 7 sampling locationsin the river during December 2014-2015 were used to develop ANFIS model. The available data set was apportioned into twoseparate sections for training and testing the ANFIS model. Developed models aimed to use the least parameters to estimateCd concentration. As a result, a relatively higher correlation (R2=0.91) was found between observed and modelled Cdconcentrations. The results indicated that the ANFIS model gave reasonable estimates for the concentrations of Cd with a highdegree accuracy and robustness. In conclusion, this paper suggests that ANFIS methodology produce very successful findingsand has the ability to predict Cd concentration in water resources. The outcomes of this research provide more information,simulation, and prediction about heavy metal concentration in natural aquatic ecosystems. Therefore, ANFIS can be used infurther researches on water quality monitoring.
Anahtar Kelime:

Belge Türü: Makale Makale Türü: Araştırma Makalesi Erişim Türü: Erişime Açık
  • Ahmed, A.A.M., & Shah, S.M.A. (2015). Application of adaptive neuro-fuzzy inference system (ANFIS) to estimate the biochemical oxygen demand (BOD) of Surma River. Journal of King Saud University - Engineering Sciences, 29(3), 237-243. https://dx.doi.org/10.1016/j.jksues.2015.02.001
  • Akkoyunlu, A., Altun, H., & Cigizoglu, H.K. (2011). Depth-Integrated Estimation of Dissolved Oxygen in a Lake. Journal of Environmental Engineering, 137(10), 961-967. https://dx.doi.org/10.1061/(ASCE)/EE,1943-7870.0000376
  • Akoto, O., Bruce, T.N., & Darko, G. (2008). Heavy metals pollution profiles in streams serving the Owabi reservoir. African Journal of Environmental Science and Technology, 2(11), 354-359
  • Akpomie, T.M., Ekanem, E.O., Adamu, M.M., & AkpoFmie, J.O. (2016). Computer Modelling of the Concentration of Heavy Metals in Artificial Borings. World Journal of Analytical Chemistry, 4(1), 6-10. https://dx.doi.org/10.12691/wjac-4-1-2
  • Alam, M.G.M., Tanaka, A., Stagnitti, F., Allinson, G., & Maekawa, T. (2001). Observations on the Effects of Caged Carp Culture on Water and Sediment Metal Concentrations in Lake Kasumigaura, Japan. Ecotoxicology and Environmental Safety, 48(1), 107-115. https://dx.doi.org/10.1006/eesa.2000.1989
  • Almasri, M.N., & Kaluarachchi, J.J. (2005). Modular neural networks to predict the nitrate distribution in ground water using the on-ground nitrogen loading and recharge data. Environmental Modelling & Software, 20(7), 851-871. https://dx.doi.org/10.1016/j.envsoft.2004.05.001
  • Alte, P.D., & Sadgir, P.A. (2015). Water Quality Prediction By Using ANN. International Journal of Advance Foundation And Research In Science & Engineering (IJAFRSE), 1, 278-285
  • Altunkaynak, A., Özger, M., & Çakmakcı, M. (2005). Fuzzy logic modeling of the dissolved oxygen fluctuations in Golden Horn. Ecological Modelling, 189(3-4), 436-446. https://dx.doi.org/10.1016/j.ecolmodel.2005.03.007
  • Areerachakul, S. (2012). Comparison of ANFIS and ANN for Estimation of Biochemical Oxygen Demand Parameter in Surface Water. International Journal of Environmental, Chemical, Ecological, Geological and Geophysical Engineering, 6(4), 168-172. https://dx.doi.org/scholar.waset.org/1999.6/3706 A y, M., & Kisi, O. (2011). Modeling of Dissolved Oxygen Concentration Using Different Neural Network Techniques in Foundation Creek, El Paso County, Colorado. Journal of Environmental Engineering, 138(6), 654-662. https://dx.doi.org/1943-7870.0000511.10.1061/(ASCE)EE
  • Ay, M., & Kisi, O. (2014). Modelling of chemical oxygen demand by using ANNs, ANFIS and k-means clustering techniques. Journal of Hydrology, 511, 279-289. https://dx.doi.org/10.1016/j.jhydrol.2014.01.054
  • Bayatzadeh Fard, Z., Ghadimi, F., & Fattahi, H. (2017). Use of artificial intelligence techniques to predict distribution of heavy metals in groundwater of Lakan lead-zinc mine in Iran. Journal of Mining &Environment, 8(1), 35-48. https://dx.doi.org/10.22044/jme.2016.592
  • Bisht, D.C.S., & Jangid, A. (2011). Discharge Modelling using Adaptive Neuro - Fuzzy Inference System. International Journal of Advanced Science and Technology, 31(1), 99-114
  • Chang, F.-J., Chung, C.-H., Chen, P.-A., Liu, C.-W., Coynel, A., & Vachaud, G. (2014). Assessment of arsenic concentration in stream water using neuro fuzzy networks with factor analysis. Science of The Total Environment, 494-495, 202-210. https://dx.doi.org/10.1016/j.scitotenv.2014.06.133
  • Chen, Q., Morales-Chaves, Y., Li, H., & Mynett, A.E. (2006). Hydroinformatics techniques in eco-environmental modelling and management. Journal of Hydroinformatics, 8(4), 297-316. https://dx.doi.org/10.2166/hydro.2006.011
  • Chen, W.B., & Liu, W.C. (2014). Artificial neural network modeling of dissolved oxygen in reservoir. Environmental Monitoring and Assessment, 186(2), 1203-1217. https://dx.doi.org/10.1007/s10661-013-3450-6
  • Csábrági, A., Molnár, S., Tanos, P., & Kovács, J. (2015). Forecasting of dissolved oxygen in the river danube using neural networks. Hungarian Agricultural Engineering,(27), 38-41. https://dx.doi.org/10.17676/hae.2015.27.38
  • Csábrági, A., Molnár, S., Tanos, P., & Kovács, J. (2017). Application of artificial neural networks to the forecasting of dissolved oxygen content in the Hungarian section of the river Danube. Ecological Engineering, 100, 63-72. https://dx.doi.org/10.1016/j.ecoleng.2016.12.027
  • Dahiya, S., Singh, B., Gaur, S., Garg, V.K., & Kushwaha, H.S. (2007). Analysis of groundwater quality using fuzzy synthetic evaluation. Journal of Hazardous Materials, 147(3), 938-946. https://dx.doi.org/10.1016/j.jhazmat.2007.01.119
  • Dogan, E., Sengorur, B., & Koklu, R. (2009). Modeling biological oxygen demand of the Melen River in Turkey using an artificial neural network technique. Journal of Environmental Management, 90(2), 1229-1235. https://dx.doi.org/10.1016/j.jenvman.2008.06.004
  • El Badaoui, H., Abdallaoui, A., Manssouri, I., & Lancelot, L. (2013). Application of the artificial neural networks of MLP type for the prediction of the levels of heavy metals in Moroccan aquatic sediments. International Journal of Computational Engineering Research, 3(6), 75-81
  • Elhatip, H., & Kömür, M.A. (2008). Evaluation of water quality parameters for the Mamasin dam in Aksaray City in the central Anatolian part of Turkey by means of artificial neural networks. Environmental Geology, 53(6), 1157-1164. https://dx.doi.org/10.1007/s00254-007-0705-y
  • Emamgholizadeh, S., Kashi, H., Marofpoor, I., & Zalaghi, E. (2014). Prediction of water quality parameters of Karoon River (Iran) by artificial intelligence-based models. International Journal of Environmental Science and Technology, 11(3), 645-656. https://dx.doi.org/10.1007/s13762-013-0378-x
  • Ghadimi, F. (2015). Prediction of heavy metals contamination in the groundwater of Arak region using artificial neural network and multiple linear regression. Journal of Tethys, 3(3), 203-215
  • Hanbay, D., Turkoglu, I., & Demir, Y. (2008). Prediction of wastewater treatment plant performance based on wavelet packet decomposition and neural networks. Expert Systems with Applications, 34(2), 1038-1043. https://dx.doi.org/10.1016/j.eswa.2006.10.030
  • Heddam, S. (2014). Modeling hourly dissolved oxygen concentration (DO) using two different adaptive neuro-fuzzy inference systems (ANFIS): a comparative study. Environmental Monitoring and Assessment, 186(1), 597-619. https://dx.doi.org/10.1007/s10661-013-3402-1
  • Hisar, O., Sönmez, A.Y., Kaya, H., & Aras Hisar, Ş. (2012). Various inference systems for classification of water quality status: A case study. Marine Science and Technology Bulletin, 1(1), 7-11
  • Icaga, Y. (2007). Fuzzy evaluation of water quality classification. Ecological Indicators, 7(3), 710-718. https://dx.doi.org/10.1016/j.ecolind.2006.08.002
  • Jang, J.-S.R. (1993). ANFIS : Adap tive-Ne twork-Based Fuzzy Inference System. IEEE Transactions on Systems, Man, and Cybernetics, 23(3), 665-685
  • Jang, J.-S.R., Sun, C.-T., & Mizutani, E. (1997). Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence. 1 ed. New Jersey, USA, Prentice Hall. 614 pp.
  • Jayalakshmi, T., & Santhakumaran, A. (2011). Statistical Normalization and Back Propagation for Classification. International Journal of Computer Theory and Engineering, 3(1), 89-93
  • Kanda, E.K., Kipkorir, E.C., & Kosgei, J.R. (2016). Dissolved oxygen modelling using artificial neural network: a case of River Nzoia, Lake Victoria basin, Kenya. Journal of Water Security, 2(1). https://dx.doi.org/10.15544/jws.2016.004
  • Kemper, T., & Sommer, S. (2002). Estimate of Heavy Metal Contamination in Soils after a Mining Accident Using Reflectance Spectroscopy. Environmental Science & Technology, 36(12), 2742-2747. https://dx.doi.org/10.1021/es015747j
  • Keskin, T.E., Düğenci, M., & Kaçaroğlu, F. (2015). Prediction of water pollution sources using artificial neural networks in the study areas of Sivas, Karabük and Bartın (Turkey). Environmental Earth Sciences, 73(9), 5333-5347. https://dx.doi.org/10.1007/s12665-014-3784-6
  • Khadr, M., & Elshemy, M. (2016). Data-driven modeling for water quality prediction case study: The drains system associated with Manzala Lake, Egypt. Ain Shams Engineering Journal, 1-9. https://dx.doi.org/10.1016/j.asej.2016.08.004
  • Kisi, O. (2005). Suspended sediment estimation using neuro-fuzzy and neural network approaches. Hydrological Sciences Journal, 50(4), 683-696. https://dx.doi.org/10.1623/hysj.2005.50.4.683
  • Kisi, O., & Ay, M. (2012). Comparison ANN and ANFIS techniques in modeling dissolved oxygen. Proceeding of the Sixteenth International Water Technology Conference, IWTC-16 (pp. 1-10). İstanbul, Turkey.
  • Kucukali, S. (2008). Forecasting the river discharge, thermal and sediment load characteristics: a case study for Filyos River. Proceeding of 15th River Flow Conference (pp. 1975-1981). Çeşme, İzmir, Turkey, Kubaba Congress Department and Travel Services.
  • Kucukali, S. (2014). Environmental risk assessment of small hydropower (SHP) plants: A case study for Tefen SHP plant on Filyos River. Energy for Sustainable Development, 19, 102-110. https://dx.doi.org/10.1016/j.esd.2013.12.010
  • Lermontov, A., Yokoyama, L., Lermontov, M., & Machado, M.A.S. (2009). River quality analysis using fuzzy water quality index: Ribeira do Iguape river watershed, Brazil. Ecological Indicators, 9(6), 1188-1197. https://dx.doi.org/10.1016/j.ecolind.2009.02.006
  • Nemati, S., Naghipour, L., & Fazeli Fard, M.H. (2014). Artificial Neural Network Modeling of Total Dissolved Solid in the Simineh River, Iran. Journal of Civil Engineering and Urbanism, 4(1), 8-14
  • Nemati, S., Fazelifard, M.H., Terzi, Ö., & Ghorbani, M.A. (2015). Estimation of dissolved oxygen using data-driven techniques in the Tai Po River, Hong Kong. Environmental Earth Sciences, 74(5), 4065-4073. https://dx.doi.org/10.1007/s12665-015-4450-3
  • Ocampo-Duque, W., Ferré-Huguet, N., Domingo, J.L., & Schuhmacher, M. (2006). Assessing water quality in rivers with fuzzy inference systems: A case study. Environment International, 32(6), 733-742. https://dx.doi.org/10.1016/j.envint.2006.03.009
  • Olayinka, K.O., & Alo, B.I. (2004). Studies on Industrial Pollution in Nigeria: The effect of Textile effluents on the quality of Groundwater in some parts of Lagos. Nigerian Journal of Health and Biomedical Sciences, 3(1), 44-50. https://dx.doi.org/10.4314/njhbs.v3i1.11507
  • Piotrowski, A.P., Napiorkowski, M.J., Napiorkowski, J.J., & Osuch, M. (2015). Comparing various artificial neural network types for water temperature prediction in rivers. Journal of Hydrology, 529, 302-315. https://dx.doi.org/10.1016/j.jhydrol.2015.07.044
  • Qasaimeh, A., Abdallah, M., & Bani Hani, F. (2012). Adaptive Neuro-Fuzzy Logic System for Heavy Metal Sorption in Aquatic Environments. Journal of Water Resource and Protection, 04(05), 277-284. https://dx.doi.org/10.4236/jwarp.2012.45030
  • Ranković, V., Radulović, J., Radojević, I., Ostojić, A., & Čomić, L. (2010). Neural network modeling of dissolved oxygen in the Gruža reservoir, Serbia. Ecological Modelling, 221(8), 1239-1244. https://dx.doi.org/10.1016/j.ecolmodel.2009.12.023
  • Ranković, V., Radulović, J., Radojević, I., Ostojić, A., & Čomić, L. (2012). Prediction of dissolved oxygen in reservoirs using adaptive network-based fuzzy inference system. Journal of Hydroinformatics, 14(1), 167-179. https://dx.doi.org/10.2166/hydro.2011.084
  • Rehana, S., & Mujumdar, P.P. (2009). An imprecise fuzzy risk approach for water quality management of a river system. Journal of Environmental Management, 90(11), 3653-3664. https://dx.doi.org/10.1016/j.jenvman.2009.07.007
  • Rooki, R., Doulati Ardejani, F., Aryafar, A., & Bani Asadi, A. (2011). Prediction of heavy metals in acid mine drainage using artificial neural network from the Shur River of the Sarcheshmeh porphyry copper mine, Southeast Iran. Environmental Earth Sciences, 64(5), 1303-1316. https://dx.doi.org/10.1007/s12665-011-0948-5
  • Seker, D.Z., Kaya, S., Musaoglu, N., Kabdasli, S., Yuasa, A., & Duran, Z. (2005). Investigation of meandering in Filyos River by means of satellite sensor data. Hydrological Processes, 19(7), 1497-1508. https://dx.doi.org/10.1002/hyp.5593
  • Sengorur, B., Dogan, E., Koklu, R., & Samandar, A. (2006). Dissolved oxygen estimation using artificial neural network for water quality control. Fresenius Environmental Bulletin, 15(9), 1064-1067
  • Singh, K.P., Basant, A., Malik, A., & Jain, G. (2009). Artificial neural network modeling of the river water quality—A case study. Ecological Modelling, 220(6), 888-895. https://dx.doi.org/10.1016/j.ecolmodel.2009.01.004
  • Soyupak, S., Karaer, F., Gürbüz, H., Kivrak, E., Sentürk, E., & Yazici, A. (2003). A neural network-based approach for calculating dissolved oxygen profiles in reservoirs. Neural Computing & Applications, 12(3-4), 166-172. https://dx.doi.org/10.1007/s00521-003-0378-8
  • Sönmez, A.Y., Hisar, O., & Yanık, T. (2012). Determination of Heavy Metal Pollution in Karasu River and Classification of Water Quality. Atatürk Üniversitesi Ziraat Fakültesi Dergisi, 43(1), 69-77 Sönmez, A.Y., Hasiloglu, S., Hisar, O., Aras Mehan, H.N., & Kaya, H. (2013a). Fuzzy Logic Evaluation of Water Quality Classification for Heavy Metal Pollution in Karasu Stream, Turkey. Ekoloji, 22(87), 43-50. https://dx.doi.org/10.5053/ekoloji.2013.876
  • Sönmez, A.Y., Hisar, O., & Yanık, T. (2013b). 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., Kadak, A., Özdemir, R.C., & Bilen, S. (2016). Establishing on Heavy Metal Accumulation in Some Economically Important Fish Species Captured from Kastamonu Costal. Alınteri Journal of Agricultural Sciences, 31 (2), 84-90. http://dergipark.gov.tr/alinterizbd/issue/24983/285156
  • Terzi, Ö., Keskin, M.E., & Taylan, E.D. (2006). Estimating evaporation using ANFIS. Journal of Irrigation and Drainage Engineering, 132(5), 503-207. https://dx.doi.org/10.1061/共ASCE兲0733-9437共2006兲132:5共503兲
  • Valente, T., Ferreira, M.J., Grande, J.A., de la Torre, M.L., & Borrego, J. (2013). pH, electric conductivity and sulfate as base parameters to estimate the concentration of metals in AMD using a fuzzy inference system. Journal of Geochemical Exploration, 124, 22-28. https://dx.doi.org/10.1016/j.gexplo.2012.07.013
  • Wang, W.-C., Chau, K.-W., Cheng, C.-T., & Qiu, L. (2009). A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series. Journal of Hydrology, 374(3-4), 294-306. https://dx.doi.org/10.1016/j.jhydrol.2009.06.019
  • Yesilnacar, M.I., & Sahinkaya, E. (2012). Artificial neural network prediction of sulfate and SAR in an unconfined aquifer in southeastern Turkey. Environmental Earth Sciences, 67(4), 1111-1119. https://dx.doi.org/10.1007/s12665-012-1555-9
  • Yılmaz Öztürk, B., Akköz, C., Aşıkkutlu, B., & Gümüş, N.E. (2014). Fuzzy Logic Evaluation of Heavy Metal Pollution of Apa Dam Lake. Journal of Applied Biological Sciences, 8(3), 14-20
  • Zhao, Y., Nan, J., Cui, F.-y., & Guo, L. (2007). Water quality forecast through application of BP neural network at Yuqiao reservoir. Journal of Zhejiang University-SCIENCE A, 8(9), 1482-1487. https://dx.doi.org/10.1631/jzus.2007.A1482
APA SÖNMEZ A, KALE S, Ozdemir R, KADAK A (2018). An Adaptive Neuro-Fuzzy Inference System (ANFIS) to Predict of Cadmium (Cd) Concentrations in the Filyos River, Turkey. , 1333 - 1343. 10.4194/1303-2712-v18_12_01
Chicago SÖNMEZ Adem Yavuz,KALE Semih,Ozdemir Rahmi Can,KADAK Ali Eslem An Adaptive Neuro-Fuzzy Inference System (ANFIS) to Predict of Cadmium (Cd) Concentrations in the Filyos River, Turkey. (2018): 1333 - 1343. 10.4194/1303-2712-v18_12_01
MLA SÖNMEZ Adem Yavuz,KALE Semih,Ozdemir Rahmi Can,KADAK Ali Eslem An Adaptive Neuro-Fuzzy Inference System (ANFIS) to Predict of Cadmium (Cd) Concentrations in the Filyos River, Turkey. , 2018, ss.1333 - 1343. 10.4194/1303-2712-v18_12_01
AMA SÖNMEZ A,KALE S,Ozdemir R,KADAK A An Adaptive Neuro-Fuzzy Inference System (ANFIS) to Predict of Cadmium (Cd) Concentrations in the Filyos River, Turkey. . 2018; 1333 - 1343. 10.4194/1303-2712-v18_12_01
Vancouver SÖNMEZ A,KALE S,Ozdemir R,KADAK A An Adaptive Neuro-Fuzzy Inference System (ANFIS) to Predict of Cadmium (Cd) Concentrations in the Filyos River, Turkey. . 2018; 1333 - 1343. 10.4194/1303-2712-v18_12_01
IEEE SÖNMEZ A,KALE S,Ozdemir R,KADAK A "An Adaptive Neuro-Fuzzy Inference System (ANFIS) to Predict of Cadmium (Cd) Concentrations in the Filyos River, Turkey." , ss.1333 - 1343, 2018. 10.4194/1303-2712-v18_12_01
ISNAD SÖNMEZ, Adem Yavuz vd. "An Adaptive Neuro-Fuzzy Inference System (ANFIS) to Predict of Cadmium (Cd) Concentrations in the Filyos River, Turkey". (2018), 1333-1343. https://doi.org/10.4194/1303-2712-v18_12_01
APA SÖNMEZ A, KALE S, Ozdemir R, KADAK A (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. 10.4194/1303-2712-v18_12_01
Chicago SÖNMEZ Adem Yavuz,KALE Semih,Ozdemir Rahmi Can,KADAK Ali Eslem 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, no.12 (2018): 1333 - 1343. 10.4194/1303-2712-v18_12_01
MLA SÖNMEZ Adem Yavuz,KALE Semih,Ozdemir Rahmi Can,KADAK Ali Eslem 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, vol.18, no.12, 2018, ss.1333 - 1343. 10.4194/1303-2712-v18_12_01
AMA SÖNMEZ A,KALE S,Ozdemir R,KADAK A 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. 2018; 18(12): 1333 - 1343. 10.4194/1303-2712-v18_12_01
Vancouver SÖNMEZ A,KALE S,Ozdemir R,KADAK A 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. 2018; 18(12): 1333 - 1343. 10.4194/1303-2712-v18_12_01
IEEE SÖNMEZ A,KALE S,Ozdemir R,KADAK A "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, ss.1333 - 1343, 2018. 10.4194/1303-2712-v18_12_01
ISNAD SÖNMEZ, Adem Yavuz vd. "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 (2018), 1333-1343. https://doi.org/10.4194/1303-2712-v18_12_01