Research Article
PDF Zotero Mendeley EndNote BibTex Cite

Modelling Nitrate Prediction of Groundwater and Surface Water Using Artificial Neural Networks

Year 2018, Volume 21, Issue 2, 321 - 325, 01.06.2018
https://doi.org/10.2339/politeknik.385434

Abstract

This study aims to estimate the changes in the amount of nitrate in Yeşilırmak Watershed using surface water and underground water of the nitrate content determined by General Directorate of State Hydraulic Works using Artificial Neural Networks (ANNs). This study was conducted in 2010 at 30 stations (9 groundwater, 18 surface water and 3 closed water source) in Yeşilırmak Watershed. Nitrate ranged from 0.341 to 77.700 mg/L, with an average value of 17.870 mg/L. In this study, changes in the amount of nitrate in Amasya using groundwater and surface water in the basin of the nitrate content determined by the Provincial Directorate of Agriculture modeling was presented with an approach based on Artificial Neural Networks (ANNs) and predict the nitrate value for the year of 2020 and 2030. Thus, the nitrate levels of water samples obtained from 30 stations water supplies found to be under the limits of Turkish and international codex of drinking water intended for human consumption.

References

  • [1] McNeely R.N., Neimanis V.P. and Dwyer L., “Water quality sourcebook: a guide to water quality parameters”. Indland Waters Directorate, Water Quality Branch, Ottawa. (1979).
  • [2] Ritter W.F. and Chirnside A.E.M., “Impact of land use on groundwater quality in Southern Delaware”, Ground Water, 22: 38–47, (1984).
  • [3] Hem J.D., “Study and interpretation of the chemical characteristics of natural water L: US”, Geological Survey water-supply paper 2254. US Geological Survey, Alexandria, (1985).
  • [4] WHO, “Health hazards from nitrate in drinking-water. Report on a WHO meeting”, Copenhagen, 5–9 March 1984. Copenhagen, WHO Regional Office for Europe (Environmental Health Series No. 1). (1985).
  • [5] Ecetoc, “Nitrate and drinking water”. Brussels, European Centre for Ecotoxicology and Toxicology of Chemicals (Technical Report No. 27). (1988).
  • [6] Wise, “Water Note 3 Groundwater at Risk: Managing the water under us”, http://ec.europa.eu/environment/water/participation/pdf/waternotes/water_note3_groundwateratrisk.pdf (2016).
  • [7] Technical Assistance for the Implementation of Nitrate Directive, “Technical Assistance for the Implementation of Nitrates Directive in Turkey”. http://nitrat.tarim.gov.tr/TarimsalCevre.Portal. (2016).
  • [8] Büyük G., Erhan A.K.Ç.A., Takashi K.U.M.E. and Nagano T., “Investigation of Nitrate Pollution in Groundwater Used for Irrigation in Konya Karapinar Region, Central Anatolia”, Kahramanmaraş Sütçü İmam Üniversitesi Doğa Bilimleri Dergisi, 19(2): 168-173. (2016).
  • [9] Ertaş N., Gönülalan Z., Yıldırım Y., Serhat A.L. and Karadal F., “Kayseri Bölgesi Kuyu Sularındaki Nitrat ve Nitrit Düzeyleri”. Journal of Faculty of Veterinary Medicine, Erciyes University, 10(1), (2013).
  • [10] Bulut, C., Atay, R., Uysal, K., and Köse, E., “Çivril Gölü yüzey suyu kalitesinin değerlendirilmesi”, Anadolu Üniversitesi Bilim ve Teknoloji Dergisi–C, Yaşam Bilimleri ve Biyoteknoloji, 2: 1-8. (2012).
  • [11] O’Shea, L., and Wade, A. “Controlling nitrate pollution: An integrated approach”, Land Use Policy, 26(3): 799-808, (2009).
  • [12] Ribbe L., Delgado P., Salgado E. and Flügel W.A., “Nitrate pollution of surface water induced by agricultural non-point pollution in the Pocochay watershed, Chile”, Desalination, 226(1): 13-20, (2008).
  • [13] Strik D., Domnanovich A.M., Zani L., Braun R. and Holubar P., “Prediction of trace compounds in biogas from anaerobic digestion using the Matlab Neural Network Toolbox”. Environ. Modell. Softw. 20:803–10, (2005).
  • [14] Şengörür B. and Öz C., “Determination of the effects of water pollution of aquacultures using neural networks”. Turkish Journal of Engineering and Environmental Sciences, 26(2): 95-106, (2002).
  • [15] Garcet J.P., Ordonez A., Roosen J. and Vanclooster M., “Metamodelling: Theory, concepts and application to nitrate leaching modelling”, Ecological Modelling, 193(3): 629-644, (2006).
  • [16] Yesilnacar M.I., Sahinkaya E., Naz M. and Ozkaya B., “Neural network prediction of nitrate in groundwater of Harran Plain, Turkey”, Environmental Geology, 56(1): 19-25, (2008).
  • [17] O’Shea L. and Wade A., “Controlling nitrate pollution: An integrated approach”, Land Use Policy, 26(3): 799-808, (2009).
  • [18] Kunkel R., Kreins P., Tetzlaff B. and Wendland F., “Forecasting the effects of EU policy measures on the nitrate pollution of groundwater and surface waters”, Journal of Environmental Sciences, 22(6): 872-877, (2010).
  • [19] Peña-Haro S., Llopis-Albert C., Pulido-Velazquez M. and Pulido-Velazquez D., “Fertilizer standards for controlling groundwater nitrate pollution from agriculture: El Salobral-Los Llanos case study, Spain”, Journal of Hydrology, 392(3): 174-187, (2010).
  • [20] İleri S., Karaer F., Katip A., Onur S.S. and Aksoy E., “Assessment of some pollution parameters with geographic information system (GIS) in sediment samples of Lake Uluabat, Turkey”, Journal of Biological and Environmental Sciences, 8(22), (2014).
  • [21] Aguilera P.A., Frenich A.G., Torres J.A., Castro H., Vidal J.M. and Canton M., “Application of the Kohonen neural network in coastal water management: methodological development for the assessment and prediction of water quality”, Water Research, 35(17): 4053-4062, (2001).
  • [22] Palani S., Liong S.Y. and Tkalich P., “An ANN application for water quality forecasting”, Marine Pollution Bulletin, 56(9): 1586-1597, (2008).
  • [23] Singh K.P., Basant A., Malik A. and Jain G., “Artificial neural network modeling of the river water quality—a case study”, Ecological Modelling, 220(6): 888-895, (2009).
  • [24] State Institute of Statistics, “Statistical Year Book of Turkey”. DIE, Ankara. (2000).
  • [25] DSI, “Yeşilırmak Basin Plans”, Superior Water Quality Monitoring. http://www.yesilirmak.org.tr/userfiles/file/HARITA_20_1.pdf, (2016).
  • [26] Lobbrecht A.H., Dibike Y.B. and Solomatine D.P., “Applications of Neural Networks and Fuzzy Logic to Integrated Water Management” 173. Project Report. (2002).
  • [27] Öztemel E., “Artificial Neural Networks”, Papatya Press, İstanbul, (2006).
  • [28] TS 266, “Water intended for human consumption”, Turkish Standards Institution. (2005).
  • [29] Council Directive 98/83/EC, “Official Journal of the European Communities. on the quality of water intended for human consumption”. L 330/32. (1998).

Modelling Nitrate Prediction of Groundwater and Surface Water Using Artificial Neural Networks

Year 2018, Volume 21, Issue 2, 321 - 325, 01.06.2018
https://doi.org/10.2339/politeknik.385434

Abstract

This study aims to estimate the changes in the amount of nitrate in Yeşilırmak Watershed using surface water and underground water of the nitrate content determined by General Directorate of State Hydraulic Works using Artificial Neural Networks (ANNs). This study was conducted in 2010 at 30 stations (9 groundwater, 18 surface water and 3 closed water source) in Yeşilırmak Watershed. Nitrate ranged from 0.341 to 77.700 mg/L, with an average value of 17.870 mg/L. In this study, changes in the amount of nitrate in Amasya using groundwater and surface water in the basin of the nitrate content determined by the Provincial Directorate of Agriculture modeling was presented with an approach based on Artificial Neural Networks (ANNs) and predict the nitrate value for the year of 2020 and 2030. Thus, the nitrate levels of water samples obtained from 30 stations water supplies found to be under the limits of Turkish and international codex of drinking water intended for human consumption.

References

  • [1] McNeely R.N., Neimanis V.P. and Dwyer L., “Water quality sourcebook: a guide to water quality parameters”. Indland Waters Directorate, Water Quality Branch, Ottawa. (1979).
  • [2] Ritter W.F. and Chirnside A.E.M., “Impact of land use on groundwater quality in Southern Delaware”, Ground Water, 22: 38–47, (1984).
  • [3] Hem J.D., “Study and interpretation of the chemical characteristics of natural water L: US”, Geological Survey water-supply paper 2254. US Geological Survey, Alexandria, (1985).
  • [4] WHO, “Health hazards from nitrate in drinking-water. Report on a WHO meeting”, Copenhagen, 5–9 March 1984. Copenhagen, WHO Regional Office for Europe (Environmental Health Series No. 1). (1985).
  • [5] Ecetoc, “Nitrate and drinking water”. Brussels, European Centre for Ecotoxicology and Toxicology of Chemicals (Technical Report No. 27). (1988).
  • [6] Wise, “Water Note 3 Groundwater at Risk: Managing the water under us”, http://ec.europa.eu/environment/water/participation/pdf/waternotes/water_note3_groundwateratrisk.pdf (2016).
  • [7] Technical Assistance for the Implementation of Nitrate Directive, “Technical Assistance for the Implementation of Nitrates Directive in Turkey”. http://nitrat.tarim.gov.tr/TarimsalCevre.Portal. (2016).
  • [8] Büyük G., Erhan A.K.Ç.A., Takashi K.U.M.E. and Nagano T., “Investigation of Nitrate Pollution in Groundwater Used for Irrigation in Konya Karapinar Region, Central Anatolia”, Kahramanmaraş Sütçü İmam Üniversitesi Doğa Bilimleri Dergisi, 19(2): 168-173. (2016).
  • [9] Ertaş N., Gönülalan Z., Yıldırım Y., Serhat A.L. and Karadal F., “Kayseri Bölgesi Kuyu Sularındaki Nitrat ve Nitrit Düzeyleri”. Journal of Faculty of Veterinary Medicine, Erciyes University, 10(1), (2013).
  • [10] Bulut, C., Atay, R., Uysal, K., and Köse, E., “Çivril Gölü yüzey suyu kalitesinin değerlendirilmesi”, Anadolu Üniversitesi Bilim ve Teknoloji Dergisi–C, Yaşam Bilimleri ve Biyoteknoloji, 2: 1-8. (2012).
  • [11] O’Shea, L., and Wade, A. “Controlling nitrate pollution: An integrated approach”, Land Use Policy, 26(3): 799-808, (2009).
  • [12] Ribbe L., Delgado P., Salgado E. and Flügel W.A., “Nitrate pollution of surface water induced by agricultural non-point pollution in the Pocochay watershed, Chile”, Desalination, 226(1): 13-20, (2008).
  • [13] Strik D., Domnanovich A.M., Zani L., Braun R. and Holubar P., “Prediction of trace compounds in biogas from anaerobic digestion using the Matlab Neural Network Toolbox”. Environ. Modell. Softw. 20:803–10, (2005).
  • [14] Şengörür B. and Öz C., “Determination of the effects of water pollution of aquacultures using neural networks”. Turkish Journal of Engineering and Environmental Sciences, 26(2): 95-106, (2002).
  • [15] Garcet J.P., Ordonez A., Roosen J. and Vanclooster M., “Metamodelling: Theory, concepts and application to nitrate leaching modelling”, Ecological Modelling, 193(3): 629-644, (2006).
  • [16] Yesilnacar M.I., Sahinkaya E., Naz M. and Ozkaya B., “Neural network prediction of nitrate in groundwater of Harran Plain, Turkey”, Environmental Geology, 56(1): 19-25, (2008).
  • [17] O’Shea L. and Wade A., “Controlling nitrate pollution: An integrated approach”, Land Use Policy, 26(3): 799-808, (2009).
  • [18] Kunkel R., Kreins P., Tetzlaff B. and Wendland F., “Forecasting the effects of EU policy measures on the nitrate pollution of groundwater and surface waters”, Journal of Environmental Sciences, 22(6): 872-877, (2010).
  • [19] Peña-Haro S., Llopis-Albert C., Pulido-Velazquez M. and Pulido-Velazquez D., “Fertilizer standards for controlling groundwater nitrate pollution from agriculture: El Salobral-Los Llanos case study, Spain”, Journal of Hydrology, 392(3): 174-187, (2010).
  • [20] İleri S., Karaer F., Katip A., Onur S.S. and Aksoy E., “Assessment of some pollution parameters with geographic information system (GIS) in sediment samples of Lake Uluabat, Turkey”, Journal of Biological and Environmental Sciences, 8(22), (2014).
  • [21] Aguilera P.A., Frenich A.G., Torres J.A., Castro H., Vidal J.M. and Canton M., “Application of the Kohonen neural network in coastal water management: methodological development for the assessment and prediction of water quality”, Water Research, 35(17): 4053-4062, (2001).
  • [22] Palani S., Liong S.Y. and Tkalich P., “An ANN application for water quality forecasting”, Marine Pollution Bulletin, 56(9): 1586-1597, (2008).
  • [23] Singh K.P., Basant A., Malik A. and Jain G., “Artificial neural network modeling of the river water quality—a case study”, Ecological Modelling, 220(6): 888-895, (2009).
  • [24] State Institute of Statistics, “Statistical Year Book of Turkey”. DIE, Ankara. (2000).
  • [25] DSI, “Yeşilırmak Basin Plans”, Superior Water Quality Monitoring. http://www.yesilirmak.org.tr/userfiles/file/HARITA_20_1.pdf, (2016).
  • [26] Lobbrecht A.H., Dibike Y.B. and Solomatine D.P., “Applications of Neural Networks and Fuzzy Logic to Integrated Water Management” 173. Project Report. (2002).
  • [27] Öztemel E., “Artificial Neural Networks”, Papatya Press, İstanbul, (2006).
  • [28] TS 266, “Water intended for human consumption”, Turkish Standards Institution. (2005).
  • [29] Council Directive 98/83/EC, “Official Journal of the European Communities. on the quality of water intended for human consumption”. L 330/32. (1998).

Details

Subjects Engineering
Journal Section Research Article
Authors

Semra BENZER This is me


Recep BENZER This is me (Primary Author)

Publication Date June 1, 2018
Application Date February 27, 2017
Published in Issue Year 2018, Volume 21, Issue 2

Cite

Bibtex @research article { politeknik385434, journal = {Politeknik Dergisi}, issn = {}, eissn = {2147-9429}, address = {Gazi Üniversitesi Teknoloji Fakültesi 06500 Teknikokullar - ANKARA}, publisher = {Gazi University}, year = {2018}, volume = {21}, pages = {321 - 325}, doi = {10.2339/politeknik.385434}, title = {Modelling Nitrate Prediction of Groundwater and Surface Water Using Artificial Neural Networks}, key = {cite}, author = {Benzer, Semra and Benzer, Recep} }
APA Benzer, S. & Benzer, R. (2018). Modelling Nitrate Prediction of Groundwater and Surface Water Using Artificial Neural Networks . Politeknik Dergisi , 21 (2) , 321-325 . DOI: 10.2339/politeknik.385434
MLA Benzer, S. , Benzer, R. "Modelling Nitrate Prediction of Groundwater and Surface Water Using Artificial Neural Networks" . Politeknik Dergisi 21 (2018 ): 321-325 <https://dergipark.org.tr/en/pub/politeknik/issue/36719/385434>
Chicago Benzer, S. , Benzer, R. "Modelling Nitrate Prediction of Groundwater and Surface Water Using Artificial Neural Networks". Politeknik Dergisi 21 (2018 ): 321-325
RIS TY - JOUR T1 - Modelling Nitrate Prediction of Groundwater and Surface Water Using Artificial Neural Networks AU - Semra Benzer , Recep Benzer Y1 - 2018 PY - 2018 N1 - doi: 10.2339/politeknik.385434 DO - 10.2339/politeknik.385434 T2 - Politeknik Dergisi JF - Journal JO - JOR SP - 321 EP - 325 VL - 21 IS - 2 SN - -2147-9429 M3 - doi: 10.2339/politeknik.385434 UR - https://doi.org/10.2339/politeknik.385434 Y2 - 2021 ER -
EndNote %0 Journal of Polytechnic Modelling Nitrate Prediction of Groundwater and Surface Water Using Artificial Neural Networks %A Semra Benzer , Recep Benzer %T Modelling Nitrate Prediction of Groundwater and Surface Water Using Artificial Neural Networks %D 2018 %J Politeknik Dergisi %P -2147-9429 %V 21 %N 2 %R doi: 10.2339/politeknik.385434 %U 10.2339/politeknik.385434
ISNAD Benzer, Semra , Benzer, Recep . "Modelling Nitrate Prediction of Groundwater and Surface Water Using Artificial Neural Networks". Politeknik Dergisi 21 / 2 (June 2018): 321-325 . https://doi.org/10.2339/politeknik.385434
AMA Benzer S. , Benzer R. Modelling Nitrate Prediction of Groundwater and Surface Water Using Artificial Neural Networks. Politeknik Dergisi. 2018; 21(2): 321-325.
Vancouver Benzer S. , Benzer R. Modelling Nitrate Prediction of Groundwater and Surface Water Using Artificial Neural Networks. Politeknik Dergisi. 2018; 21(2): 321-325.
IEEE S. Benzer and R. Benzer , "Modelling Nitrate Prediction of Groundwater and Surface Water Using Artificial Neural Networks", Politeknik Dergisi, vol. 21, no. 2, pp. 321-325, Jun. 2018, doi:10.2339/politeknik.385434
 
TARANDIĞIMIZ DİZİNLER (ABSTRACTING / INDEXING)
181341319013191 13189 13187 13188 18016