作　　者： (吕辰）; (吴宗之）; (傅贵）; (贾树泽）;

机构地区： 中国矿业大学(北京)资源与安全工程学院

出　　处： 《矿业科学学报》 2017年第4期357-363,共7页

摘　　要： PM_(2.5)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PM_(2.5)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PM_(2.5)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%,PM_(2.5)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。 There is a relationship between the variations of PM_(2.5)concentration and the happen of disaster weather as the haze,the accurate prediction of the changed trend of PM_(2.5)concentration plays an important guiding role in the effective prevention and control of the wide range of disaster weather as the haze. Considering the gray characteristics of PM_(2.5)concentration is disturbed by the various factors,the article adopts the way of the same dimension gray recurrence and supplies the new gray information in time,establishes the gray relational computing model of the main factors of PM_(2.5). At the same time,the GM( 1,N) is improved by introducing the gray control parameters,which can meet accurate prediction on the condition of multi-effects factors. The above approaches are combined,the adaptation related-combinatorial mode GCM( 1,N) is established and can be applied for different metadata N. By the analysis of the measured data obtained in Beijing,the precision of the calculation and prediction model GCM( 1,N) has reached to 89. 75% ~ 96. 44%,the relative error of the prediction of PM_(2.5) concentration is between 7. 32% and 15. 21%,the results are satisfactory.

关 键 词： 浓度 主影响因子 预测模型