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.
Skip Nav Destination
Article navigation
14 November 2023
THE 4TH INTERNATIONAL CONFERENCE ON MATERIAL SCIENCE AND APPLICATIONS
3 December 2021
Ariyalur, India
Research Article|
November 14 2023
Evaluation of river water quality by principal component analysis based water quality index and classify method using support vector machine
M. Dhurgadevi;
M. Dhurgadevi
a)
1
Mahendra Engineering College
, Namakkal, Tamilnadu, India
a)Corresponding author: [email protected]
Search for other works by this author on:
D. Vimalkumar;
D. Vimalkumar
b)
2
Hindusthan Institute of Technology
, Coimbatore, Tamilnadu, India
Search for other works by this author on:
K. Gunasekaran
K. Gunasekaran
c)
3
Sri Indhu College of Engineering
, Telengana, India
Search for other works by this author on:
AIP Conf. Proc. 2822, 020229 (2023)
Citation
M. Dhurgadevi, D. Vimalkumar, K. Gunasekaran; Evaluation of river water quality by principal component analysis based water quality index and classify method using support vector machine. AIP Conf. Proc. 14 November 2023; 2822 (1): 020229. https://doi.org/10.1063/5.0173398
Download citation file:
Pay-Per-View Access
$40.00
Sign In
You could not be signed in. Please check your credentials and make sure you have an active account and try again.
20
Views
Citing articles via
Inkjet- and flextrail-printing of silicon polymer-based inks for local passivating contacts
Zohreh Kiaee, Andreas Lösel, et al.
Effect of coupling agent type on the self-cleaning and anti-reflective behaviour of advance nanocoating for PV panels application
Taha Tareq Mohammed, Hadia Kadhim Judran, et al.
Design of a 100 MW solar power plant on wetland in Bangladesh
Apu Kowsar, Sumon Chandra Debnath, et al.
Related Content
Deep learning techniques for real-time bitcoin prediction
AIP Conf. Proc. (November 2023)
Accident alert system using block chain technology
AIP Conf. Proc. (November 2023)
A comparison of the principal component regression methods and the robust principal component regression with minimum vector variance in statistical downscaling models
AIP Conference Proceedings (December 2022)
An analysis of secure and efficient audit service for data integrity in cloud storage
AIP Conf. Proc. (November 2023)
Novel approach for mitigation of collision in mobile ad-hoc networks
AIP Conf. Proc. (November 2018)