Assessment of water quality index using cluster analysis and artificial neural network modeling: a case study of the Hooghly River basin, West Bengal, India

Abstract

River Hooghly, considered as an important tributary of the River Ganga, has been affected by indiscriminate discharging of polluted and untreated sewage sludge and industrial waste into the waterways. The assessment of water quality for natural river waters was done using a water quality index (WQI), developed by DELPHI and the Council of Ministers of the Environment methods. These two methods reflect the quality of the water measured with respect to its pollution level. Multivariate statistical techniques, such as cluster analysis, were applied to the data-set on water quality of the Hooghly River (India) which was generated during the years 2002–2008 controlling at eight different sites for five parameters. The relationships among the stations are highlighted by cluster analysis to characterize the WQI. The study represents a computer-simulated artificial neural network model for the evaluation of the relationship between the different parameters of water bodies collected at different stations along Hooghly River responsible for water quality measurement. Finally, both the water quality methods (CCME and DELPHI) were statistically compared by the coefficient of determination (R2), root mean square error, and absolute average deviation based on the validation data-set.