一种新的城市SO2污染统计预报方法及其应用
A NEW STATISTIC FORECASTINGMETHOD OF SO2 POLLUTION AND ITS APPLICATION
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摘要: 针对目前采用的统计方法存在的不足, 即在选择预报因子时没有考虑预报因子之间的相关性, 挑选的预报因子由于非正交, 使回归计算的结果不稳定, 给计算带来一定的误差。该文提出把一元线性回归分析、自然正交函数 (EOF) 和逐步回归方法结合起来, 从而得到一种新的建立统计预报模型的方法。以西安市采暖期和夏季SO2日均浓度为预报对象, 使用该方法建立预报模型。拟合及预报试验表明, 这些预报模型不但可以很好地拟合变化趋势, 而且还能作出较准确的预报, 采暖期预报的级别命中率为72.5 %, 夏季级别预报命中率为100%。通过对比试验, 此方法优于目前常用的逐步回归方法, 具有很好的应用前景。Abstract: Concerning the limits of the currently used statistic methods of air pollution (not considering correlation and non-orthogonality among forecasting factors results in regression instability and more errors), the linear regression and empirical orthogonal function (EOF) are combined with the stepwise reg ression analysis method, and thus a new fo recasting method in the building forecasting model is proposed.By using this method for forecasting SO2 density in the heating period, the model fit ting and forecasting show that these models can not only fit the changing tendency of SO2 density, but also fo recast SO2 densi ty qui te well, e.g., the grade accuracy is 72.5 percent.In cont rast with the step wise reg ression analysis method, during the forecast experiment, the result of the new forecasting method is more accurate. The new forecasting method has good prospect in application.
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表 1 1999年采暖期滚动预报模型的统计特征量
表 2 逐步回归与主成分逐步回归SO2浓度预报结果相对误差的比较
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