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

Semra Benzer

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.

Keywords

ARTIFICIAL NEURAL NETWORKS FOR FORECAST OF THE AMOUNT OF HEAVY METAL

References

• Gazi University, Gazi Faculty of Education, Department of Science Education, Ankara, Turkey
• Gazi University, Institute of Information, Department of Forensics Computing Ankara, Turkey