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The study on the accuracy of classifiers for water quality application

Dirty water is the world's biggest health risk. When water from rain roads into rivers, it picks up toxic chemicals, dirt, trash and disease-carrying organisms along the way. Many of our water resources lack basic protections, making them vulnerable to pollution from factory farms and indust...

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Main Authors: Rosaida, Rosly, Mokhairi, Makhtar, Mohd Khalid, Awang, M Nordin, A Rahman, Mustafa, Mat Deris
Format: Article
Language:English
Published: 2015
Subjects:
T Technology (General)
TC Hydraulic engineering. Ocean engineering
Online Access:http://eprints.unisza.edu.my/4979/1/FH02-FIK-15-03850%20%281%29.pdf
http://eprints.unisza.edu.my/4979/
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spelling my-unisza-ir.49792022-01-31T03:05:47Z http://eprints.unisza.edu.my/4979/ The study on the accuracy of classifiers for water quality application Rosaida, Rosly Mokhairi, Makhtar Mohd Khalid, Awang M Nordin, A Rahman Mustafa, Mat Deris T Technology (General) TC Hydraulic engineering. Ocean engineering Dirty water is the world's biggest health risk. When water from rain roads into rivers, it picks up toxic chemicals, dirt, trash and disease-carrying organisms along the way. Many of our water resources lack basic protections, making them vulnerable to pollution from factory farms and industrial plants. Due to that, a classification model is needed to present the quality of the water environment. In this paper, the data mining techniques are used in this research by applying the classification method for water quality application. Various classifiers were studied in order to find the most accurate classifier for the dataset. This paper presents the comparison of accuracies for the five classifiers (NB, MLP, J48, SMO, and IBk) based on a 10-fold cross validation as a test method with respect to water quality from the datasets of Kinta River, Perak Malaysia. This study also explores which classifier is suitable to classify the dataset. The selected attributes used in this study were: DO Sat, DO Mgl, BOD Mgl, COD Mgl, TS Mgl, DO Index, AN Index, SS Index, Class, and Degree of pollution. The data consisted of 166 instances and obtained from the East Coast Environmental Research Institute (ESERI) of Universiti Sultan Zainal Abidin (UniSZA). The result of MLP and IBk performed better than other classifiers for Kinta River dataset because these classifiers showed the highest accuracy with the same percentage of 91.57%. In the future, we will propose the multiclassifier approach by introducing a fusion at a classification level between these classifiers to get a higher accuracy of classification. 2015-09 Article PeerReviewed text en http://eprints.unisza.edu.my/4979/1/FH02-FIK-15-03850%20%281%29.pdf Rosaida, Rosly and Mokhairi, Makhtar and Mohd Khalid, Awang and M Nordin, A Rahman and Mustafa, Mat Deris (2015) The study on the accuracy of classifiers for water quality application. International Journal of u- and e- Service, Science and Technology, 8 (3). pp. 145-154. ISSN 2005-4246
institution Universiti Sultan Zainal Abidin
building UNISZA Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Sultan Zainal Abidin
content_source UNISZA Institutional Repository
url_provider https://eprints.unisza.edu.my/
language English
topic T Technology (General)
TC Hydraulic engineering. Ocean engineering
spellingShingle T Technology (General)
TC Hydraulic engineering. Ocean engineering
Rosaida, Rosly
Mokhairi, Makhtar
Mohd Khalid, Awang
M Nordin, A Rahman
Mustafa, Mat Deris
The study on the accuracy of classifiers for water quality application
description Dirty water is the world's biggest health risk. When water from rain roads into rivers, it picks up toxic chemicals, dirt, trash and disease-carrying organisms along the way. Many of our water resources lack basic protections, making them vulnerable to pollution from factory farms and industrial plants. Due to that, a classification model is needed to present the quality of the water environment. In this paper, the data mining techniques are used in this research by applying the classification method for water quality application. Various classifiers were studied in order to find the most accurate classifier for the dataset. This paper presents the comparison of accuracies for the five classifiers (NB, MLP, J48, SMO, and IBk) based on a 10-fold cross validation as a test method with respect to water quality from the datasets of Kinta River, Perak Malaysia. This study also explores which classifier is suitable to classify the dataset. The selected attributes used in this study were: DO Sat, DO Mgl, BOD Mgl, COD Mgl, TS Mgl, DO Index, AN Index, SS Index, Class, and Degree of pollution. The data consisted of 166 instances and obtained from the East Coast Environmental Research Institute (ESERI) of Universiti Sultan Zainal Abidin (UniSZA). The result of MLP and IBk performed better than other classifiers for Kinta River dataset because these classifiers showed the highest accuracy with the same percentage of 91.57%. In the future, we will propose the multiclassifier approach by introducing a fusion at a classification level between these classifiers to get a higher accuracy of classification.
format Article
author Rosaida, Rosly
Mokhairi, Makhtar
Mohd Khalid, Awang
M Nordin, A Rahman
Mustafa, Mat Deris
author_facet Rosaida, Rosly
Mokhairi, Makhtar
Mohd Khalid, Awang
M Nordin, A Rahman
Mustafa, Mat Deris
author_sort Rosaida, Rosly
title The study on the accuracy of classifiers for water quality application
title_short The study on the accuracy of classifiers for water quality application
title_full The study on the accuracy of classifiers for water quality application
title_fullStr The study on the accuracy of classifiers for water quality application
title_full_unstemmed The study on the accuracy of classifiers for water quality application
title_sort study on the accuracy of classifiers for water quality application
publishDate 2015
url http://eprints.unisza.edu.my/4979/1/FH02-FIK-15-03850%20%281%29.pdf
http://eprints.unisza.edu.my/4979/
_version_ 1724079408443031552
score 13.211869

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