SavedAdd your journal

Journals / Politeknik Dergisi / 2018 / Cilt: 21 - Sayı: 2

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

Semra BENZER
Recep BENZER
Pages
321–325
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

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.

📌 3 Key Takeaways

🎯
Objective & Hypothesis: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).
📊
Key Findings & Data:This study was conducted in 2010 at 30 stations (9 groundwater, 18 surface water and 3 closed water source) in Yeşilırmak Watershed.
💡
Conclusion & Impact: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.
🔬Design:Modeling & Simulation
📍Scope:2010 Verileri
⚙️Variables:Artificial neural networks · nitrate · Amasya

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.

Özet

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.

🔖

Cite This Article

BENZER, S., BENZER, R. (2018). Modelling Nitrate Prediction of Groundwater and Surface Water Using Artificial Neural Networks. Politeknik Dergisi, Cilt: 21 - Sayı: 2, 321–325. https://ajindex.com/articles/307121

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (14)

  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., “D