<![CDATA[A monitoring of water quality index parameters using soft computing technology is the current research focus as the main challenge of which is to design a soft computing algorithm with the highest accuracy and less computation time. For the secondary dataset obtained by the government database, this research proposes a water quality prediction and classification method based on decision tree algorithm. The comparative analysis is made for the different highest accuracy algorithms like decision tree algorithm with support vector machine (SVM), k-nearest neighbour (KNN) classifier, linear discriminant analysis, Naïve Bayes classifier and logistic regression. Decision tree algorithm had the highest accuracy compared to other algorithms. The KNN algorithm used as clustering algorithm to plot the two classes good and bad. The trend analysis of the water quality is performed with various water quality parameters like pH, fluoride and total dissolved solids (TDS) test results are plotted and observed for the variations of the values with respect to increase in time. The performance is measured with statistical indices and the prediction accuracy of 0.99 and mean squared error of 0.05. The results prove that the KNN algorithm found to be better for clustering purposes.]]>
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