Simulating and predicting surface water quality for drinking and bathing purposes through combined approach of PCA, entropy-based WQI, and stochastic models
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Date
2024
Journal Title
Stochastic Environmental Research and Risk Assessment
Journal ISSN
Volume Title
Publisher
Springer Science and Business Media Deutschland GmbH
Abstract
Freshwater resources, specially surface water are under threat due to over extraction, discharge of pollutants and improper waste disposal. This study investigates the present status of water quality of the River Ganga at Varanasi, India and further predict its future status using Principal Component Analysis (PCA), Entropy water quality index (EWQI) and Stochastic models. To begin with, water quality data of 37 variables for eleven years were acquired followed by which PCA was applied which reduced the number of water quality variables from 37 to 13. EWQI of River Ganga was calculated for drinking and bathing purposes by using these 13 variables. Most of the physico-chemical variables were within the permissible limit. The EWQI values were calculated for all the samples indicated that none of the water sample was suitable for drinking without treatment. However, 74.24% of the samples were classified as fair, 10.60% as poor, and 15.15% as unfit for bathing. Five different time-series models with 10 different structures were accessed then compared the effectiveness of various time series models in predicting water quality with the help of data from the past eleven years. Finally, the chosen models were used to predict future water quality variables and EWQI for both drinking and bathing purposes. The optimized models were selected based on auto-correlation function and partial auto-correlation function as well as the use of Akaike information criteria and Bayesian Information Criteria. This research suggests that time series modelling can be a cost-effective and time-saving approach for long-term water quality monitoring. Graphical abstract: (Figure presented.) � The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.
Description
Keywords
Bathing, Drinking, Prediction, River water quality, Time series model