Monitoring water quality in the twenty-first century has become a major global concern. The Water Quality Index (WQI) is a useful method for determining the quality of drinking water in urban, rural, and industrial settings. Parameter selection, quality function determination for each parameter, and aggregation using mathematical equations are all part of a traditional WQI technique. A mathematical equation incorporates a number of water quality parameters to grade water quality and determine its acceptability for consumption. The dataset was treated to Principal Component Analysis (PCA) in order to extract the most essential WQI characteristics. The support vector machine technique is then used to locate outliers and categorize the water quality index. The proposed system was tested using the Southern Bug (or PivdennyiBooh) River dataset. The principal component analysis approach yielded a prediction accuracy of 95 percent, whereas the Support Vector Machine method generated a classification accuracy of 98 percent.

1.
Adimalla
,
Narsimha
, ”
Groundwater quality for drinking and irrigation purposes and potential health risks assessment: a case study from semi-arid region of South India
” in
Exposure and Health
11
(
2
),
109
123
,
2019
.
2.
Agamuthu
,
Pariatamby
,
Victor
,
Dennis
, “
Policy trends of extended producer responsibility in Malaysia
” in
Waste Management & Research
29
(
9
),
945
953
,
2011
.
3.
Aghel
,
B.
,
Rezaei
,
A.
,
Mohadesi
,
M.
, “
Modeling and prediction of water quality parameters using a hybrid particle swarm optimization–neural fuzzy approach
” in
International Journal of Environmental Science and Technology
16
(
8
),
4823
4832
.
2019
5.
Barzegar
,
Rahim
,
Mohammad
Taghi
,
Aalami
,
Jan
,
Adamowski
, “
Short-term water quality variable prediction using a hybrid CNN–LSTM deep learning model
” in
Stochastic Environmental Research and Risk Assessment
, pp.
1
19
.
2020
.
6.
Imani
,
Maryam
, et al, “
A novel machine learning application: Water quality resilience prediction Model
Science of the Total Environment
768
,
144459
.
2021
.
7.
Kar
, “
Wetlands and their Fish Diversity in Assam (India)
” in
Transylvanian Review of Systematical and Ecological Research
21
(
3
),
47
94
.
2019
.
8.
Khadr
,
Mosaad
, “Modeling of water quality parameters in Manzalalake using adaptive neuro-fuzzy inference system and stochastic models”, In:
Egyptian Coastal Lakes and Wetlands: Part II
.
Springer
, pp.
47
69
.
2017
.
9.
Oelen
,
Allard
, van Aart,
Chris
J.
,
De Boer
,
Victor
, “
Measuring surface water quality using a low-cost sensor kit within the context of Rural Africa
” In:
P-ICT4D@ WebSci.
2018
.
11.
Dhurgadevi
,
M.
,
Sakthivel
,
P.
An Analysis of Wind Energy Generation by Opting the Better Placement of Wind Turbine by Artificial Neural Network and to Improve the Energy Efficiency of Wireless Sensor Network
.
Wireless PersCommun
123
,
2607
2624
(
2022
). .
12.
Dhurgadevi
,
M.
,
Meenakshi
Devi
, P.
An Analysis of Energy Efficiency Improvement Through Wireless Energy Transfer in Wireless Sensor Network
.
Wireless PersCommun
98
,
3377
3391
(
2018
).
13.
Md. Saikat Islam
Khan
,
Nazrul
Islam
,
Jia
Uddin
,
Sifatul
Islam
,
Mostofa Kamal
Nasir
, “
Waterqualityprediction and classification based on principalcomponent regression and gradient boosting classifier approach
”,
Journal of King Saud University –Computer and Information Sciences
,
2021
.
This content is only available via PDF.
You do not currently have access to this content.