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ARTIFICIAL NEURAL NETWORKS FOR FORECAST OF THE AMOUNT OF HEAVY METAL

Year 2017, Volume: Volume 2 Issue: İssue 1 (1) - 2.İnternational Congress Of Forensic Toxicology, 23 - 23, 16.02.2017

Abstract

Heavy metals are
important environmental pollutants even at very low concentrations. The primary
sources of this pollution are the burning of fossil fuels, the mining and
smelting of metalliferous ores, municipal wastes, fertilizers, pesticides, and
sewage. These sources of pollution lead to toxic metal contamination of soil,
aqueous waste streams and groundwater which pose major environmental and health
problems. Our country is
in a critical region in terms of water resources. Thus, for the development and
preservation of water resources it is important to conduct a lot of research.
For the determination of the status of the water sources, long-term data
collection from monitoring stations, management in a common database, and the
creation of a national water quality monitoring network, are required. Also,
for the prevention of the water pollution, reliable forecasting of the amount
of heavy metal is of great importance.



In this study,
an approach for the forecast of the heavy metal values is presented with
-Artificial neural networks (ANN) which is a forecasting application used in
various areas. The suggested approach based on ANN to determine the change in
heavy metal values was applied at Ergene Basin Stations. By applying the heavy metal values for Ergene Basin Stations in
2012-2013 determined by The Ministry of Environment and
Urban Planning, the forecast of the heavy metal pollution in the basin in 2018
and 2030 were made. This study proposes that ANN can be utilized for the
forecasting of the the heavy metal values.

References

  • Gazi University, Gazi Faculty of Education, Department of Science Education, Ankara, Turkey
  • Gazi University, Institute of Information, Department of Forensics Computing Ankara, Turkey
Year 2017, Volume: Volume 2 Issue: İssue 1 (1) - 2.İnternational Congress Of Forensic Toxicology, 23 - 23, 16.02.2017

Abstract

References

  • Gazi University, Gazi Faculty of Education, Department of Science Education, Ankara, Turkey
  • Gazi University, Institute of Information, Department of Forensics Computing Ankara, Turkey
There are 2 citations in total.

Details

Journal Section Articles
Authors

Semra Benzer

Publication Date February 16, 2017
Published in Issue Year 2017 Volume: Volume 2 Issue: İssue 1 (1) - 2.İnternational Congress Of Forensic Toxicology

Cite

APA Benzer, S. (2017). ARTIFICIAL NEURAL NETWORKS FOR FORECAST OF THE AMOUNT OF HEAVY METAL. The Turkish Journal Of Occupational / Environmental Medicine and Safety, Volume 2(İssue 1 (1), 23-23.
AMA Benzer S. ARTIFICIAL NEURAL NETWORKS FOR FORECAST OF THE AMOUNT OF HEAVY METAL. turjoem. February 2017;Volume 2(İssue 1 (1):23-23.
Chicago Benzer, Semra. “ARTIFICIAL NEURAL NETWORKS FOR FORECAST OF THE AMOUNT OF HEAVY METAL”. The Turkish Journal Of Occupational / Environmental Medicine and Safety Volume 2, no. İssue 1 (1) (February 2017): 23-23.
EndNote Benzer S (February 1, 2017) ARTIFICIAL NEURAL NETWORKS FOR FORECAST OF THE AMOUNT OF HEAVY METAL. The Turkish Journal Of Occupational / Environmental Medicine and Safety Volume 2 İssue 1 (1) 23–23.
IEEE S. Benzer, “ARTIFICIAL NEURAL NETWORKS FOR FORECAST OF THE AMOUNT OF HEAVY METAL”, turjoem, vol. Volume 2, no. İssue 1 (1), pp. 23–23, 2017.
ISNAD Benzer, Semra. “ARTIFICIAL NEURAL NETWORKS FOR FORECAST OF THE AMOUNT OF HEAVY METAL”. The Turkish Journal Of Occupational / Environmental Medicine and Safety VOLUME 2/İssue 1 (1) (February 2017), 23-23.
JAMA Benzer S. ARTIFICIAL NEURAL NETWORKS FOR FORECAST OF THE AMOUNT OF HEAVY METAL. turjoem. 2017;Volume 2:23–23.
MLA Benzer, Semra. “ARTIFICIAL NEURAL NETWORKS FOR FORECAST OF THE AMOUNT OF HEAVY METAL”. The Turkish Journal Of Occupational / Environmental Medicine and Safety, vol. Volume 2, no. İssue 1 (1), 2017, pp. 23-23.
Vancouver Benzer S. ARTIFICIAL NEURAL NETWORKS FOR FORECAST OF THE AMOUNT OF HEAVY METAL. turjoem. 2017;Volume 2(İssue 1 (1):23-.