Abstract
Statistical trend analysis and time-series prediction model are widely used in water quality regulation. Using the Mann-Kendall test, trend analysis was performed on monthly time series. The monthly findings revealed that only the potential Hydrogen (pH) and Total Coliforms (TC) showed meaningful trends. Future values for the parameters which affect water quality have been predicted using the Autoregressive Integrated Moving Average (ARIMA) model. R-square, root mean square error, absolute maximum percentage error, absolute maximum error, normalised Bayesian information criteria, Ljung-Box analysis were used to validate the model. It has been found that the predictive models for potential Hydrogen (pH), Dissolved Oxygen (DO), Biochemical Oxygen Demand (BOD), and Total Coliforms (TC) are useful at 95% confidence limits. Also, the results showed that the pH values will be in the range of 7.2 to 7.5 and the predicted series were similar to the original series, providing a perfect fit. The DO (mg/l) ranges from 7.8 to 12.3 mg/l. BOD (mg/l) fluctuates continuously between 1.2 and 1.3 mg/l. The TC (MPN/100ml) values show reducing trend. The study show that the quality of water is deteriorating based on the trend for the parameters and needs managerial actions.
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