Water quality management using statistical analysis and time-series prediction model

Abstract

This paper deals with water quality management using statistical analysis and time-series prediction model. The monthly variation of water quality standards has been used to compare statistical mean, median, mode, standard deviation, kurtosis, skewness, coefficient of variation at Yamuna River. Model validated using R-squared, root mean square error, mean absolute percentage error, maximum absolute percentage error, mean absolute error, maximum absolute error, normalized Bayesian information criterion, Ljung-Box analysis, predicted value and confidence limits. Using auto regressive integrated moving average model, future water quality parameters values have been estimated. It is observed that predictive model is useful at 95 % confidence limits and curve is platykurtic for potential of hydrogen (pH), free ammonia, total Kjeldahl nitrogen, dissolved oxygen, water temperature (WT); leptokurtic for chemical oxygen demand, biochemical oxygen demand. Also, it is observed that predicted series is close to the original series which provides a perfect fit. All parameters except pH and WT cross the prescribed limits of the World Health Organization /United States Environmental Protection Agency, and thus water is not fit for drinking, agriculture and industrial use.

Applications of AutoRegressive Integrated Moving Average (ARIMA) approach in time-series prediction of traffic noise pollution

“…Several measures of accuracy were applied to ascertain the performance of the ARIMA models so developed such as stationary R 2 , R 2 , root mean square error (RMSE), mean absolute percentage error (MAPE) and normalized BIC (Bayesian information criterion). predictive model is useful at 95% confidence limits 35 . On the basis of the above discussion, it can be concluded that the model is performing satisfactory for all the single-noise metrics.…”

“…Schomer et al 30 simulations demonstrate that nonconsecutive sampling strategies reduce the overall sampling requirements for non stationary data. The exhaustive literature review reveals that ARIMA methodology has not been implemented so far for long-term noise monitoring, although it has been extensively used in air and water pollution predictions [32][33][34][35][36] . The present work extends Kumar and Jain 34 utilizing the ARIMA approach for time-series predictions and forecasting of traffic noise levels.…”

Real-time water quality monitoring through Internet of Things and ANOVA-based analysis: a case study on river Krishna

“…In the world, and especially in India, river water is the main source for all anthropogenic activities such as drinking, irrigation and agriculture (Parmar and Bhardwaj 2014;Herojeet et al 2016).The river water quality is getting degraded day by day, and water pollution is the main reason for degradation in the recent years. The water is getting polluted mainly because of rapid industrialization of river basins and river Krishna is also one among them.…”

“…These pollutants alter the physio-chemical characteristics of aquatic ecosystem because it has high concentration of BOD and TDS which cause rapid depletion of oxygen in water. It is estimated that every year, millions of tons of waste in the form of industrial waste, agricultural waste and urban waste water is dumped into the river, thus making the water from the river unusable and requires frequent quality checks (Shah and Joshi 2017;Kaur et al 2017;Parmar and Bhardwaj 2014;Loganathan and Ahamed 2017). In India, the water quality check is carried under the careful monitoring of Central Pollution Control Board (CPCB) (Nagar 2007).…”

“…This model makes use of autoregressive integrated moving average (ARIMA), R-square, root mean square error (RMSE), mean absolute percentage error (MAPE), mean absolute error (MAE) and normalized Bayesian information criterion (BIC) to find out the water quality of river Yamuna. In this analysis, it was found that water was unfit to use for drinking and agricultural purpose because it was found that the pH and water temperature (WT) parameter levels were exceeding the WHO standards limits (Parmar and Bhardwaj 2014).…”