Classifying Indian Rivers Surface Water Quality using Principal Component Analysis Combined with Principal Component Weighted Arithmetic Index Techniques

River water contamination is a major challenge in India and around the world, directly impacting not only living organism’s health but also economy of the whole community. This study did exploratory analysis by using the Principal Component analysis of water quality parameters through water quality index and classifies the quality according to the index calculated from the samples that were collected from Indian rivers. Principal component analysis (PCA), a multiple-variable decision method, is among them. PCA are mainly used for the comprehensive analysis of high dimensional data in environmental studies and also widely used in data contains temporal and spatial changes over variety of water bodies in India. From the results all samples could be evaluated by using four major principal components, which are explaining the total variability of 81.33%. Water quality was assessed using the principal component weighted Arithmetic index (PCWAI), which is defined as the sum of principal component scores weighted by their eigenvalues. These are working under the principle of Simple Additive Weighting (SAW) Model. We observed that the total quality of water in Indian rivers was stable but contamination in the state of Punjab, Odisha, Haryana, Pondicherry, and Gujarat sampling stations were of highly concern. The outcome of PCWAI explores the key sample points requiring additional control in Indian Rivers. This studyconfirmedabout interpolation of PCA and PCWAI performs an effective tool for identifying surface water quality. This tool was of highly efficient for maintaining water quality in Indian Rivers and also reduces the effects from contamination in Indian Rivers.