Time series analysis of water quality parameters at Stillaguamish River using order series method

This study applied the time series analysis approach to model and predict univariate dissolved oxygen and temperature time series for four water quality assessment stations at Stillaguamish River located in the state of Washington. The order series method was applied to fulfill the normality assumption for modeling the univariate time series. Then, the AR(I)MA models were applied to study the stationary and nonstationary time series, the Auto-Regressive Fractionally Integrated Moving Average model was applied to study the time series with long memory. The results showed there existed three different structures for the univariate water quality time series at Stillaguamish River watershed. The identified time series model for each univariate water quality time series was found to be capable of predicting future values with reasonable accuracy. Overall, the time series modeling approach may be an efficient tool in assessment of the water quality in the river system.