| 湘江流域金属污染的贝叶斯预测实证研究 |
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| 引用本文： | 刘潭秋,孙湘海.湘江流域金属污染的贝叶斯预测实证研究[J].计算技术与自动化,2015(3):78-82. |
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| 作者姓名： | 刘潭秋 孙湘海 |
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| 作者单位： | （1.长沙理工大学 经济与管理学院, 湖南 长沙410114;2.长沙理工大学 交通运输学院,湖南 长沙4100114） |
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| 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。本文选择ARIMA模型作为重金属预测模型，运用贝叶斯相关理论进行分析、参数估计和预测，从而不仅获得点预测结果，而且获得区间估计和概率预测结果。实例分析证实，基于贝叶斯方法的ARIMA模型能够获得很好的点预测和区间表现。
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| 关 键 词： | 时间序列模型 贝叶斯理论 河流重金属污染 预测 |
Forecast Study on Bayesian Forecasting Pollutant Concentration of Heavy-metal Contaminants in XiangJiang Streams |
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| LIU Tan-qiu,SUN Xiang-hai.Forecast Study on Bayesian Forecasting Pollutant Concentration of Heavy-metal Contaminants in XiangJiang Streams[J].Computing Technology and Automation,2015(3):78-82. |
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| Authors: | LIU Tan-qiu SUN Xiang-hai |
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| Affiliation: | (1.School of Management and Economics,Changsha University of Science & Technology,Changsha,Hunan410114,China;2.School of Traffic and Transportation Engineeling,Changsha University of Science & Technology, Changsha,Hunan410114,China) |
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| Abstract: | 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 as a predict model for pollutant concentration of heavy-metal contaminants in streams. In addition, Bayesian theories are applied to analyze this model, estimate parameters of this model, and give point and interval prediction results. The empirical results indicate this model perform quite well. |
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