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Prediction of water pollution sources using artificial neural networks in the study areas of Sivas, Karabük and Bartın (Turkey)

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Abstract

The determination of the rock types from which the water is recharged/discharged is an essential component of hydrochemical, hydrogeological and water pollution studies. Especially, detection of sources of groundwater contamination is very important in terms of human health and other living organism. This study aims at prediction of water pollution sources using artificial neural networks (ANNs) in Sivas, Karabük and Bartın areas of Turkey, which have different types of rocks, agricultural activity and mining activity. In this study, a model based on ANNs was developed for forecast to the water discharging from different types of rocks and the water pollution sources in the study areas. Back propagation and Bee Algorithm (BA) were used in ANN training. For achieving the aim of the study, 14 hydrochemical data set were used. The best ANN classification of water discharging from different type of rocks was accomplished with 80 % accuracy using BA. These results indicate that the researches that are similar to this study can provide quite convenience for the assessment of groundwater pollution sources when applied on a large and regional scale.

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References

  • Akbaş B, Altun İB, Aksay A (2002) 1/100 000 ölçekli Türkiye Jeoloji Haritaları Zonguldak E28 paftası (1/100.000 scaled geological map of Turkey-Zonguldak E28 section). Geological Research Department, MTA (General Directorate of Mineral Research and Exploration), No. 24, Ankara, Turkey

  • Alagha JS, Said MAM, Mogheir Y, Seyam M (2013) Modelling of chloride concentration in coastal aquifers using artificial neural network—a case study: Khanyounis Governorate Gaza Strip-Palestine. Casp J Appl Sci Res (AWAm International Conference on Civil Engineering and Geohazard Information Zonation) 2:158–165

  • Alan I, Aksay A (2002) 1/100.000 Ölçekli Türkiye Jeoloji Haritaları Zonguldak F28 Paftası (1/100.000 scaled geological map of Turkey-Zonguldak F28 section). Geological Research Department, MTA (General Directorate of Mineral Research and Exploration) No. 29, Ankara, Turkey

  • Altun İE, Senin M, Akbaş B, Keskin H, Mengi H, Köse Z, Arslan H, Deniz N, Yaşar T, Erdoğan K, Acar Ş (1994) Giresun-Piraziz-Şebinkarahisar arasında kalan bölgenin jeolojisi (Geological of the Giresun-Piraziz-Şebinkarahisar region). Geological Research Department, MTA (General Directorate of Mineral Research and Exploration), Ankara, Turkey

  • Bilginer E, Pehlivan Ş, Aksay A (2002) 1/100.000 Ölçekli Türkiye Jeoloji Haritaları Bolu-G29 Paftası (1/100.000 scaled geological map of Turkey-Bolu G29 section). Geological Research Department, MTA (General Directorate of Mineral Research and Exploration), No. 36, Ankara, Turkey

  • Camezine S, Sneyd J (1991) A model of collective nectar source selection by honey bees: self-organization through simple rules. J Theor Biol 149:547–571

    Article  Google Scholar 

  • Chang FJ, Kao LS, Kuo YM, Liu CW (2010) Artificial neural networks for estimating regional arsenic concentrations in a blackfoot disease area in Taiwan. J Hydrol 388:65–76

  • Düğenci M (2007) Arı Algoritması’nın yapay sinir ağı öğrenmesi için kullanımı ve atıksu artıma tesis kontrolü uygulaması (Using the bees algorithm for artificial neural networks training and the control application of wastewater treatment plant). PhD. Thesis Graduate School of Natural and Applied Sciences, Sakarya University, Sakarya, Turkey (unpublished)

  • Ekemen T (2001) Tecer Dağı (Sivas-Ulaş) Kaynaklarının Hidrojeoloji İncelemesi (Hydrogeological Investigation of the Tecer Mountain Springs (Sivas–Ulaş). MSc. Thesis Graduate School of Natural and Applied Sciences, Cumhuriyet University, Sivas, Turkey

  • Ekemen T (2006) Yıldız Irmağı Havzasının (Sivas) Hidrojeoloji İncelemesi [Hydrogeological Investigation of the Yıldız River Basin (Sivas)]. PhD. Thesis Graduate School of Natural and Applied Sciences, Cumhuriyet University, Sivas, Turkey

  • El-Din AG, Smith DW (2002) A neural network model to predict the wastewater inflow incorporating rainfall events. Water Res 36:115–1126

    Article  Google Scholar 

  • Fu Y, Zhao Y, Zhang Y, Guo T, He Z, Chen J (2013) GIS and ANN-based spatial prediction of DOC in river networks: a case study in Dongjiang, Southern China. Environ Earth Sci 68:1495–1505

    Article  Google Scholar 

  • Gallant SI (1993) Neural network learning and expert systems. MIT Press, Cambridge

    Google Scholar 

  • Gedik I, Aksay A (2002) 1/100 000 ölçekli Türkiye Jeoloji Haritaları Zonguldak E29 paftası (1/100.000 scaled geological map of Turkey-Zonguldak E29 section). Geological Research Department, MTA (General Directorate of Mineral Research and Exploration), No. 24, Ankara, Turkey

  • Ghassan AA, Aman J (2011) A new approach based on honeybee to improve ıntrusion detection system using neural network and Bees Algorithm. Software Engineering and Computer Systems. Commun Comput Inf Sci 181:777–792

    Article  Google Scholar 

  • Gökçe A, Özgüneylioğlu A (1988) Kurşunlu (Ortakent-Koyulhisar- Sivas) Pb–Zn–Cu yataklarının jeolojisi, oluşumu ve kökeni [Geology and genesis of the Kurşunlu (Ortakent–Koyulhisar–Sivas) Pb–Zn–Cu ore deposits]. Bulletin of the Faculty of Engineering Cumhuriyet University. Earthsci 5:23–36

    Google Scholar 

  • Gökten E (1993) Ulaş (Sivas) doğusunda Sivas Havzası güney kenarının jeolojisi: İç Toros Okyanusu’nun kapanmasıyla ilgili tektonik gelişim [Geology of the southern boundary of the Sivas Basin in the east of Ulaş (Sivas); tectonic development related to the closure of the Inner tauride Ocean]. Bull Turk Assoc Pet Geol 5:35–55

    Google Scholar 

  • Gürsoy H (1986) Örenlice-Eskiköy (Sivas) Yöresinin Stratigrafik ve Tektonik Özellikleri [Tectonic and Stratigraphical Features of Örenlice-Eskiköy (Sivas) Region]. MSc. Thesis Graduate School of Natural and Applied Sciences, Cumhuriyet University, Sivas, Turkey

  • Hagan MT, Demuth HB, Beale MH (1996) Neural network design. PWS Publishing, Boston

    Google Scholar 

  • Hamed MM, Khalafallah MG, Hassanien EA (2004) Prediction of wastewater treatment plant performance using artificial neural networks. Environ Model Softw 19(10):919–928

    Article  Google Scholar 

  • İnan N (1987) Tecer Dağının (Sivas) Jeolojik Özellikleri ve Foraminiferlerinin Sistematik İncelemesi [Geological Features of the Tecer Mountain (Sivas) and Systematic Investigation of the its Foraminiferas]. PhD. Thesis Graduate School of Natural and Applied Sciences, Cumhuriyet University, Sivas, Turkey

  • İnan S, Öztürk A, Gürsoy H (1993) Ulaş-Sincan (Sivas) yöresinin stratigrafisi [Stratigraphy of the Ulaş-Sincan (Sivas) region]. Turk J Earth Sci 2:1–15

    Google Scholar 

  • Jaafar O, Toriman MEH, Idris MH, Mastura SAS, Juahir HH, Aziz NAA, Kamarudin KA, Jamil NR (2010) Study of water level-discharge relationship using artificial neural network (ANN) in Sungai Gumum, Tasik Chini Pahang Malaysia. Res J Appl Sci 5:20–26

  • Jeong CH (2001) Mineral-water interaction and hydrogeochemistry in the Samkwang mine area, Korea. Geochem J 35:1–12

    Article  Google Scholar 

  • Jeong D, Kim YO (2005) Rainfall-runoff models using artificial neural networks for ensemble streamflow prediction. Hydrol Process 19:3819–3835

  • Karaboga D, Öztürk C (2009) Neural networks training by artificial bee colony algorithm on pattern classification. Neural Netw World 19:279–292

    Google Scholar 

  • Keskin TE (2010a) Nitrate and heavy metal pollution resulting from agricultural activity: a case study from Eskipazar (Karabuk, Turkey). Environ Earth Sci 61:703–721

    Article  Google Scholar 

  • Keskin TE (2010b) Ground water changes in relation to seismic activity: a case study from Eskipazar (Karabuk, Turkey). Hydrogeol J 18:1205–1218

    Article  Google Scholar 

  • Keskin TE (2013) Mineral-water interaction and hydrogeochemistry of groundwater around Bartın coal mine, Turkey. Fresenius Environ Bull 22(9a):2750–2762

    Google Scholar 

  • Keskin TE, Toptaş S (2012) Heavy metal pollution in the surrounding ore deposits and mining activity: a case study from Koyulhisar (Sivas–Turkey). Environ Earth Sci 67:859–866

    Article  Google Scholar 

  • Kumar J, Jain A, Srivastava R (2006) Neural network based solutions for locating groundwater pollution sources. Hydro J 29:55–66

    Google Scholar 

  • Kuo YM, Liu CW, Lin KH (2004) Evaluation of the ability of an artificial neural network model to assess the variation of groundwater quality in an area of blackfoot disease in Taiwan. Water Res 38:148–158

  • Moasheri SA, Tabatabaie SM, Razaghi P, Sarani N, Abadi SHEM (2012) Estimating the groundwater nitrate by using artificial neural network and optimizing it by genetic algorithm. International Conference on Transport, Environment and Civil Engineering (ICTECE’2012) August 25–26, Kuala Lumpur (Malaysia)

  • MTA (2009) 1/100 000 Ölçekli Türkiye jeoloji haritaları-Giresun G39, G40, H39, H40 paftaları (1/100.000 scaled geological map of Turkey–Giresun G39, G40, H39, H40 sections). Geological Research Department, MTA (General Directorate of Mineral Research and Exploration), Ankara, Turkey (unpublished)

  • Pham DT, Ghanbarzadeh A, Koc E, Otri S, Rahim S, Zaidi M (2005) Bees Algorithm, Technical Note. Manufacturing Engineering Centre, Cardiff University, Cardiff

    Google Scholar 

  • Pham DT, Ghanbarzadeh A, Koc E, Otri S, Rahim S, Zaidi M (2006) The Bees Algorithm–A novel tool for complex optimisation problems. In Proceedings of 2nd International Virtual Conference on Intelligent Production Machines and Systems (IPROMS2006) Oxford:Elsevier

  • Reidel SP, Johnson VG, Spane FA (2002) Natural gas storage in basalt aquifers of the Columbia Basin, Pacific Northwest USA—a guide to site characterization: Pacific Northwest Laboratory, Richland, Washington, Report PNNL-13962

  • Republic of Turkey Ministry of Health (2005) İnsani Tüketim Amaçlı Sular Hakkında Yönetmelik (Turkish regulation concerning water intended for human consumption). Republic of Turkey Ministry of Health, Ankara

    Google Scholar 

  • Sahoo GB, Ray C, Mehnert E, Keefer DA (2006) Application of artificial neural networks to assess pesticide contamination in shallow groundwater. Sci Total Environ 367:234–251

  • Şaroğlu F, Herece E, Sarıaslan M, Emre Ö (1995) Yeniçağ-Eskipazar-Gerede arasının jeolojisi ve Kuzey Anadolu Fayı’nın genel özellikleri (Geological of the Yeniçağ–Eskipazar–Gerede region and general features of North Anatolian Fault Zone). MTA Report no. 9873, Ankara, Turkey

  • Seeley TD (1996) The Wisdom of the Hive: the Social physiology of honey bee colonies. Harvard University Press, Cambridge

    Google Scholar 

  • Sevin M, Aksay A (2002) 1/100.000 Ölçekli Türkiye Jeoloji Haritaları Bolu-G28 Paftası (1/100.000 scaled geological map of Turkey-Bolu G28 section). Geological Research Department, MTA (General Directorate of Mineral Research and Exploration), No. 35, Ankara, Turkey

  • Seyam M, Mogheir Y (2011) Application of artificial neural networks model as analytical tool for groundwater salinity. J Environ Prot 2:56–71

  • Taşdemir Y, Kolay E, Kayabalı K (2013) Comparison of three artificial neural network approaches for estimating of slake durability index. Environ Earth Sci 68:23–31

    Article  Google Scholar 

  • Timur E, Aksay A (2002) 1/100.000 Ölçekli Türkiye Jeoloji Haritaları Zonguldak F29 Paftası (1/100.000 scaled geological map of Turkey-Zonguldak F29 section). Geological Research Department, MTA (General Directorate of Mineral Research and Exploration) No. 30, Ankara, Turkey

  • Tokay M (1973) Kuzey Anadolu Fay Zonunun Gerede ile Ilgaz arasındaki kısmında jeolojik gözlemler (Geological observations on the North Anatolian Fault Zone in the area between Gerede Ilgaz). Symposium on the north Anatolian fault and earthquake belt, Ankara, Turkey, Bulletin of MTA Institute 12–29

  • Uysal Ş, Bedi Y, Kurt İ, Kılınç F (1995) Koyulhisar (Sivas) dolayının jeolojisi [Geological of the Koyulhisar (Sivas) vicinity]. Geological Research Department, MTA (General Directorate of Mineral Research and Exploration), Ankara-Turkey

  • Verma AK, Singh T (2013) Prediction of water quality from simple field parameters. Environ Earth Sci 69:821–829

    Article  Google Scholar 

  • Vlassopoulos D, Goin J, Zeliff M, Porcello J, Tolan T, Lindsey K (2009) Groundwater geochemistry of the Columbia River basalt group aquifer system: Columbia Basin groundwater management area of Adams, Franklin, Grant, and Lincoln Counties. Columbia River basalt group aquifer system: Columbia Basin groundwater management area of Adams, Franklin, Grant, and Lincoln Counties, Othello, Washington, June 2009

  • Von Frisch K (1967) Bees: thesir vision, chemical senses, and language. Cornell Paperbacks publishing, New York

    Google Scholar 

  • WHO (2006) Guidelines for drinking-water quality. First addendum to third edition, 1, Recommendation. WHO, Geneva

    Google Scholar 

  • Yağmur M (1996) Yıldızdağ (Sivas kuzeyi) Gabrosunun Mineralojik-Petragrafik ve Jeokimyasal İncelenmesi [Mineralogical-Petrographic and Geochemical Investigation of Yıldızdağ (North of Sivas) Gabbro]. M.Sc. Thesis Graduate School of Natural and Applied Sciences, Cumhuriyet University, Sivas, Turkey

  • Yesilnacar MI, Sahinkaya E, Naz M, Ozkaya B (2008) Neural network prediction of nitrate in groundwater of Harran Plain, Turkey. Environ Geol 56:19–25

    Article  Google Scholar 

  • Yılmaz A (1982) Dumanlıdağı (Tokat) ile Çeltekdağı (Sivas) arasının temel jeoloji özellikleri ve ofiyolitli karışığın konumu [The main geological features of the section between Dumanlıdağ (Tokat) and Çeltekdağı (Sivas) and the position of ophiolitic melange]. MTA (General Directorate of Mineral Research and Exploration) Report No: 7230, Ankara, Turkey

  • Yılmaz A, Uysal Ş, Ağan A, Göç, Aydın N (1997) 1/100.000 Ölçekli Açınsama Nitelikli Türkiye Jeoloji Haritaları Sivas-F23 Paftası. (1/100.000 scaled geological map of Turkey-Sivas F23 section). Geological Research Department, MTA (General Directorate of Mineral Research and Exploration) No: 47, Ankara, Turkey

  • Zhang Q, Stanley SJ (1997) Forecasting raw water quality parameters for the North-Saskatchewan River by neural network modeling. Water Res 31:2340–2350

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Acknowledgments

The authors would like to thank the Cumhuriyet University Scientific Research Projects Commission (CÜBAP) for providing financial support for all research projects performed in Sivas, Karabük and Bartın areas.

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Authors and Affiliations

  1. Environmental Engineering Department, Karabük University, 78050, Karabuk, Turkey

    Tülay Ekemen Keskin

  2. Industrial Engineering Department, Karabük University, 78050, Karabuk, Turkey

    Muharrem Düğenci

  3. Geology Engineering Department, Muğla Sıtkı KOÇMAN University, 48000, Muğla, Turkey

    Fikret Kaçaroğlu

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  1. Tülay Ekemen Keskin
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  2. Muharrem Düğenci
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  3. Fikret Kaçaroğlu
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Correspondence to Tülay Ekemen Keskin.

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Keskin, T.E., Düğenci, M. & Kaçaroğlu, F. Prediction of water pollution sources using artificial neural networks in the study areas of Sivas, Karabük and Bartın (Turkey). Environ Earth Sci 73, 5333–5347 (2015). https://doi.org/10.1007/s12665-014-3784-6

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