基于一种时间序列模型的河流重金属污染浓度预测研究

摘要

水环境是一个充满不确定性的复杂巨系统,传统水质模型很难体现重金属污染物在河流中迁移的随机性,因此经典的时间序列模型-ARIMA模型被应用于河流重金属污染浓度的预测.实例分析证实,通过采用将获得的最新数据不断地添加到用于模型设定的样本中,并再此基础上获得最近向前一个时期预测值的动态预测方法,ARIMA模型能够获得很好的预测表现,尤其是在充分考虑模型残差统计分布特征的情况下,采用具有学生t分布的模型预测更精确.%Traditional stream water-quality models are hardly able to describe stochastic behavior of heavy-metal contaminants in water, due to stream environment influenced by various uncertainties. Therefore, a classic time series model, namely autoregressive integrated moving average (ARIMA) model, is used to predict pollutant concentration of heavy-metal contaminants in streams. An empirical analysis evaluates the forecasting performance of two ARIMA models with different statistical distribution errors using a dynamic forecast approach. The results indicate that the two ARIMA models both perform very well, especially the one with student t distribution.