Ground Water Quality Management Using Time Series Prediction Model

ABSTRACT

This paper addresses the critical importance of assessing the quality of raw water before treatment as a determinant for predicting the subsequent quality and natural attributes of mineral water intended for public consumption. The study introduces a comprehensive model for managing raw water quality within a water treatment plant. Specifically, the investigation focuses on the temporal trends of pH and Total Dissolved Substances (TDS) in the raw water entering the treatment facility. A time series model is developed to monitor the pH and TDS of the raw water, aiming to evaluate whether the treatment plant can sustain water quality without the need for additional additives. The research employs R-console and Eviews software for data analysis. Results reveal discernible upward and downward trends in the pH and TDS series over time. In response, an ARIMA (3,0,4) model is proposed for both the pH and TDS series, serving as a predictive tool to generate forecasts. The findings suggest that the pH and TDS values of the raw water indicate the treatment plant’s capability to consistently produce quality water over time without the reliance on external additives. This research contributes to advancing the understanding and management of raw water quality in water treatment processes, offering insights that can enhance the sustainability of water treatment practices.

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