The transfer function was introduced to establish the prediction method for the DO concentration at the intaking point of Kongju Water Works System. In the most cases we analyze a single time series without explicitly using information contained in the related time series. In many forecasting situations, other events will systematically influence the series to be forecasted(the dependent variables), and therefore, there is need to go beyond a univariate forecasting model. Thus, we must build a forecasting model that incorporates more than one time series and introduces explicitly the dynamic characteristics of the system. Such a model is called a multiple time series model or transfer function model.
The purpose of this study is to develop the stochastic stream water quality model for the intaking station of Kongju city waterworks in Keum river system.
The performance of the multiplicative ARIMA model and the transfer function noise model were examined through comparisons between the historical and generated monthly dissolved oxygen series. The result reveal that the transfer function noise model lead to the improved accuracy.

• 류병로(대전산업대학교 환경공학과) | Byong Ro Ryu
• 조정석(대구대학교 토목공학과) | Chung Seok Cho
• 한양수(대구대학교 토목공학과) | Yang Su Han