摘要：According to the current situation of water quality in drainage basin, the key to improve the prediction accuracy is to select the appropriate prediction model of water quality. The time series method excellently reflected the continuity of the future data in the case of emphasizing historical data. What’s more, the time series method has the higher short-term prediction accuracy and simple modeling process. So, the time series method was used to establish the Auto-Regressive and Moving Average (ARMA) model for the time series of the concentration of dissolved oxygen (DO), biochemical oxygen demand (BOD5), chemical oxygen demand (CODCr), ammonia nitrogen (NH3-N) and total nitrogen (TN) at the Guidu fu section of Qingyi River from January 2011 to December 2015. Then, the concentrations of the five water quality indicators from January to June 2016 were predicted, which were verified and analyzed with the measured values. The results show that the model has fine fitting effect and higher prediction accuracy, which can accurately reflect the current and future change trends of the water quality.
其他摘要：According to the current situation of water quality in drainage basin, the key to improve the prediction accuracy is to select the appropriate prediction model of water quality. The time series method excellently reflected the continuity of the future data in the case of emphasizing historical data. What’s more, the time series method has the higher short-term prediction accuracy and simple modeling process. So, the time series method was used to establish the Auto-Regressive and Moving Average (ARMA) model for the time series of the concentration of dissolved oxygen (DO), biochemical oxygen demand (BOD5), chemical oxygen demand (CODCr), ammonia nitrogen (NH3-N) and total nitrogen (TN) at the Guidu fu section of Qingyi River from January 2011 to December 2015. Then, the concentrations of the five water quality indicators from January to June 2016 were predicted, which were verified and analyzed with the measured values. The results show that the model has fine fitting effect and higher prediction accuracy, which can accurately reflect the current and future change trends of the water quality.
