Akademik Veri Yönetim Sistemi
3rd International Congress of Engineering and Natural Sciences Studies, Ankara, Türkiye, 24 - 25 Mayıs 2023, ss.62
Water
quality parameters are important measures of the health and safety of water
sources, which can be affected by various natural and human-induced factors.
There are several parameters to assess water quality. The aim of this study is
to group 17 water stations in the Ergene Basin, Turkiye by using k-means and
fuzzy c-means clustering algorithms which are methods of unsupervised machine
learning. For this reason, 15 water related variables from the period of
1985-2013 are used to group 17 water stations. Different number of clusters are
inspected in both of the algorithms and the optimal number of clusters is found
as 4. These clusters are named as high-quality water, slightly polluted water, polluted
water, and highly polluted water. The selected water parameters are Biochemical
oxygen demand (BOD5), Chloride (Cl-), Dissolved oxygen (DO), Escherichia coli
(EC), Aluminum (Al), Ammonium–nitrogen (NH4-N), Nitrite–nitrogen (NO2-N),
Nitrate–nitrogen (NO3-N), Orthophosphate (o-PO4), Potential of Hydrogen (pH),
Photovoltaics (pV), Suspended Solid (SS), Temperature (T), Total Dissolved Solid
(TDS), Turbidity (Turb).
The center of the clusters are
used to identify the characteristics of stations. The first cluster has the
lowest BOD5, Al, NO2-N, T average, and the highest DO average. The second
cluster has the lowest Cl-, EC, NH4-N, o-PO4, pV, SS, TDS and Turb average, and
the highest NO3-N, pH and T average. The third cluster has the lowest DO average,
and has the highest Cl-, EC, Al, NH4-N, NO2-N, o-PO4 and TDS average. The
fourth cluster has the lowest NO3-N and pH average, and has the highest BOD5,
pV, SS and Turb average.