Prediction of Water Quality Using Principal Component Analysis

The groundwater is contaminated heavily with acidity, alkalinity, toxicity, heavy minerals, and microbes throughout the world due to population growth, urbanization and industrialization. Hence, evaluation of water quality of groundwater is extremely important to prepare for remedial measures. This paper presents application of an empirical approach for classification of water samples based on 10 quality parameters of water. In this research work, water samples from 10 sources in three different years and seasons have been collected to assess the quality of water. Q-mode principal component analysis has been applied to classify the water samples into four different categories considering parameters such as pH, DO, turbidity, TDS, hardness, calcium ion (Ca ++ ), chloride ion (Cl − ), BOD, iron (Fe ++ ), sulfate ( ). This classification will be useful for the planners and field engineers for taking ameliorative measures in advance for preventing the contamination of groundwater. The non-parametric method proposed here efficiently assesses water quality index for classification of water quality. The model can also be used for estimating water quality on-line but the accuracy of the model depends upon the judicious selection of parameters.