Lake Bera and Lake Chini water quality monitoring using support vector machine / Siti Fatihah Asy Syura Mat Jubit - Siti Fatihah Asy Syura, Mat Jubit (2012)
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%.