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基于GCM(1,N)自适应关联组合模型的PM2.5浓度预测

吕辰 吴宗之 傅贵 贾树泽

吕辰, 吴宗之, 傅贵, 贾树泽. 基于GCM(1,N)自适应关联组合模型的PM2.5浓度预测[J]. 矿业科学学报, 2017, 2(4): 357-363.
引用本文: 吕辰, 吴宗之, 傅贵, 贾树泽. 基于GCM(1,N)自适应关联组合模型的PM2.5浓度预测[J]. 矿业科学学报, 2017, 2(4): 357-363.
Lü Chen, Wu Zongzhi, Fu Gui, Jia Shuze. The prediction of PM2.5 concentration based on the adaptation related-combinatorial mode GCM(1,N)[J]. Journal of Mining Science and Technology, 2017, 2(4): 357-363.
Citation: Lü Chen, Wu Zongzhi, Fu Gui, Jia Shuze. The prediction of PM2.5 concentration based on the adaptation related-combinatorial mode GCM(1,N)[J]. Journal of Mining Science and Technology, 2017, 2(4): 357-363.

基于GCM(1,N)自适应关联组合模型的PM2.5浓度预测

基金项目: 国家自然科学基金(51534008);“十三五”国家重点研发计划重点专项(2016YFC0801501,2016YFC0801505)
详细信息
    作者简介:

    吕辰(1989—),男,安徽淮南人,博士研究生,主要从事事故灾害控制理论与技术研究。

  • 中图分类号: X956

The prediction of PM2.5 concentration based on the adaptation related-combinatorial mode GCM(1,N)

  • 摘要: PM2.5浓度的变化与雾霾灾害天气的发生有着内在的必然联系,准确预测PM2.5浓度变化趋势,对有效防治大范围雾霾灾害天气的发生具有重要指导作用.本研究根据PM2.5浓度受多因素扰动的灰特性,采用等维灰递补的方法,及时补充新的灰信息,构建了PM2.5主影响因子灰关联计算模型;同时引入灰控制参数对GM(1,N)模型进行改进,满足多影响因素条件下的精确预测,将二者结合建立了适应于不同N元的GCM(1,N)自适应预测模型.通过对北京地区实测数据的应用和分析,GCM(1,N)计算预测模型精度达到89.75%~96.44%,PM2.5浓度预测相对误差在7.32%~15.21%之间,取得了较好的预测效果.
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出版历程
  • 收稿日期:  2016-08-21
  • 刊出日期:  2017-07-25

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