Lake Bera and Lake Chini water quality monitoring using support vector machine / Siti Fatihah Asy Syura Mat Jubit
Water quality monitoring is very important to control the quality of water. Lake Bera and Lake Chini which are known as a very important wetland are used to apply SVM method to predict its water quality. The output used to predict the classification of high medium and low is the dissolved oxygen...
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| Format: | Thesis |
| Published: |
2012
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| Online Access: | http://studentsrepo.um.edu.my/3799/1/1._Title_page%2C_abstract%2C_content.pdf http://studentsrepo.um.edu.my/3799/2/2._Chapter_1_%E2%80%93_6.pdf http://studentsrepo.um.edu.my/3799/3/3._Appendices%2C_References.pdf http://pendeta.um.edu.my/client/default/search/results?qu=Lake+Bera+and+Lake+Chini+water+quality+monitoring+using+support+vector+machine&te= http://studentsrepo.um.edu.my/3799/ |
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| Summary: | Water quality monitoring is very important to control the quality of water. Lake Bera and
Lake Chini which are known as a very important wetland are used to apply SVM method
to predict its water quality. The output used to predict the classification of high medium
and low is the dissolved oxygen according to the standard provided by the Interim
National Water Quality Standard of Malaysia and Department of Environment. The
training and test data is divided to 80% for training data and 20% for testing data. The
SVM is implemented using R software package kernlab which used ksvm as its
implementation to do prediction. Kernel Anova was used to create the model. The result
shows that the predicted accuracy is about 74%. |
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