Generalized Autoregressive Conditional Heteroskedastic Model for Water Quality Analyses and Time Series Investigation in Reservoir Watersheds

A vector time series is coupled with both the Generalized Autoregressive Conditional Heteroskedastic (GARCH) model and an impact response analyses of the multiple time series Vector Autoregressive Moving Average (VARMA) model in this research to investigate the time series variation of organic pollution factors. The analyses target three organic pollution factors, that is, dissolved oxygen (DO), biochemical oxygen demand (BOD), and ammonia nitrogen (NH sub(3)-N), for understanding their time series influence pattern and responses among the various water quality parameters. After model matching of the many vectors, the optimal matching model combination, VARMA(1,0,1)-GARCH(1,1), was selected for depicting the time series dependence of the selected pollutant factors. Results of impulse response analyses reveal that BOD responds immediately to changes of current DO and that the consumption of DO is not obvious during the initial stage of NH sub(3)-N decomposition. During the one time lag period, NH sub(3)-N is further oxidized into nitrite and nitrate to cause obvious increase of DO consumption. In this article, the statistical technology is used to develop the VARMA-GARCH integration model for simulating and predicting the water quality using data collected in the watershed of northern Taiwan. Therefore, the internal mechanism and the significance represented by the process of constructing the model can be expanded. The model proposed in this research will allow the user to grasp the instantaneous changes of the time series water quality in the watershed. Results will provide valuable references for the water quality authority to implement timely and effective water management measures in response to changes of water quality.