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: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
2015
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| Subjects: | |
| 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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| Summary: | 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. |
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