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中国农学通报 ›› 2013, Vol. 29 ›› Issue (32): 221-224.doi: 10.11924/j.issn.1000-6850.2012-3509

• 工程 机械 水利 装备 • 上一篇    下一篇

基于ARMA模型的地表水水质预测方法研究?

杜鑫 吴钢 许东   

  • 收稿日期:2012-10-27 修回日期:2012-11-18 出版日期:2013-11-15 发布日期:2013-11-15
  • 基金资助:
    国家水体污染控制与治理科技重大专项

Prediction Methods Analysis of the Water Quality Based on the ARMA Model

  • Received:2012-10-27 Revised:2012-11-18 Online:2013-11-15 Published:2013-11-15

摘要: 选用合适的模型提高预测的精度和可靠性,为区域水环境管理提供科学依据,是水质预测要解决的关键问题。为了解决这一问题,根据辽河流域的实际,运用自回归滑动平均(ARMA)模型对辽河流域东陵大桥监测断面CODMn的水质变化趋势进行预测。结果表明:综合自相关函数、偏相关函数以及BIC原则,ARMA(1,1)模型能够更好地用于东陵大桥断面水质预测。拟合结果显示,相对误差在2.60%~25.98%之间,平均相对误差为13.69%,说明该模型能够充分利用近期水质资料信息,以精确预测未来水质变化趋势。而对东陵大桥监测断面CODMn的预测显示,未来CODMn呈现出增长态势,辽宁水环境管理任务仍然很重。最后,就ARMA模型应用于水质预测的问题和发展方向进行了探讨。

关键词: 吸附等温线, 吸附等温线

Abstract: Appropriate choice of a model to improve the accuracy and reliability of the forecast to provide a scientific basis for regional water quality management, is the key to solve the problem of water quality prediction. In order to solve this problem, the change values of CODMn of the Dongling Bridge monitoring section were forecasted in Liaohe Basin, according to the Liaohe Basin reality with the auto-regressive moving average (ARMA) model. The results showed that: the ARMA (1,1) model was the most appropriate model for the water quality prediction of the Dongling Bridge section by comprehensive consideration of the autocorrelation function, partial autocorrelation function and the BIC principle. The fitting results showed that the relative error was between 2.60% and 25.98%, the average relative error was 13.69%, indicating that the model could accurately predict future water quality trends by taking full advantage of recent water quality data. The forecast of the values of CODMn of Dongling Bridge monitoring sections showed that future CODMn was growing, water environment management tasks were still hard in Liaoning Province. Finally, the problems and development directions of ARMA model application in the prediction of water quality were discussed.

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