Authors :
V. Queen Jemila; M. Dhanalakshmi; M.Amutha
Volume/Issue :
Volume 9 - 2024, Issue 1 - January
Google Scholar :
http://tinyurl.com/37a3tu3r
Scribd :
http://tinyurl.com/ycybt9nj
DOI :
https://doi.org/10.5281/zenodo.10496097
Abstract :
The main work of our research aims to find out
the Water Quality Index of bore water in our
surrounding educational institutions using two machine
learning algorithms. Our research work differentiates
from other work by choosing Decision Tree, K-Nearest
Neighbor algorithms, and their accuracy. We collected
water samples from various resources and calculated the
six important factors: salinity, total suspended solids
(TDS), dissolved oxygen (DO), acidity and alkalinity (pH),
and biochemical oxygen demand (BOD). Using efficient
chemical methods, the quality parameters of water were
examined. We created our dataset by utilizing these
metrics, and the dataset is given as our chosen algorithm’s
training and testing data. Finally, we got the WQI value
with two different accuracies.
Keywords :
Water quality Index, Decision Tree, KNN, Gini Index.
The main work of our research aims to find out
the Water Quality Index of bore water in our
surrounding educational institutions using two machine
learning algorithms. Our research work differentiates
from other work by choosing Decision Tree, K-Nearest
Neighbor algorithms, and their accuracy. We collected
water samples from various resources and calculated the
six important factors: salinity, total suspended solids
(TDS), dissolved oxygen (DO), acidity and alkalinity (pH),
and biochemical oxygen demand (BOD). Using efficient
chemical methods, the quality parameters of water were
examined. We created our dataset by utilizing these
metrics, and the dataset is given as our chosen algorithm’s
training and testing data. Finally, we got the WQI value
with two different accuracies.
Keywords :
Water quality Index, Decision Tree, KNN, Gini Index.
