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首页> 外文期刊>Environmental Monitoring and Assessment >Evaluation of the temporal scaling variability in forecasting ground-level ozone concentrations obtained from multiple linear regressions
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Evaluation of the temporal scaling variability in forecasting ground-level ozone concentrations obtained from multiple linear regressions

机译：从多个线性回归获得的预测地面臭氧浓度的时间尺度变化的评估

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摘要

Ozone is a highly unpredictable pollutant which severely affects living conditions in urban and surrounding areas in the Mediterranean basin. This secondary pollutant periodically reaches extremely high concentrations, damaging human health. Multiple linear regression has been widely used in previous works due to the fact that it is a simple and versatile method for forecasting ozone concentrations. However, these models usually prove their validity using fulfillment of statistical constraints, ignoring other intrinsic characteristics existing in the time series, such as the temporal scaling behavior and the data distribution over different time scales. In previous works, it has been demonstrated that observed ozone time series are of a multifractal nature, meaning that the data distribution can be described by using the multifractal spectrum. This work focuses on the capacity of a forecasting model to reproduce the scaling features existing in an observed time series when several chemical and meteorological explanatory variables are introduced following the stepwise procedure. A comparison between the observed spectrum and the simulated ones for each step is used to check which explanatory variables better reproduce the multifractal nature in real ozone time series. It has been confirmed that a model with few explanatory variables allows reproducing the multifractal nature in the simulated time series with an acceptable accuracy without compromising the values of the coefficient of determination and root-mean-squared error, which were used as performance indicators.
机译：臭氧是一种高度不可预测的污染物，会严重影响地中海盆地城市和周边地区的生活条件。这种二次污染物会定期达到极高的浓度，从而危害人类健康。由于多元线性回归是一种简单而通用的预测臭氧浓度的方法，因此已在以前的工作中广泛使用。但是，这些模型通常通过满足统计约束条件来证明其有效性，而忽略了时间序列中存在的其他固有特征，例如时间尺度行为和不同时间尺度上的数据分布。在以前的工作中，已经证明观察到的臭氧时间序列具有多重分形特征，这意味着可以通过使用多重分形谱来描述数据分布。这项工作着重于在逐步程序中引入几个化学和气象解释变量时，预测模型能够再现观察到的时间序列中存在的缩放特征的能力。将每个步骤的观测光谱与模拟光谱进行比较，以检查哪些解释变量可以更好地重现真实臭氧时间序列中的多重分形性质。业已确认，解释变量很少的模型可以在模拟的时间序列中以可接受的精度重现多重分形性质，而不会损害用作性能指标的测定系数和均方根误差的值。

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  • 来源
    《Environmental Monitoring and Assessment》 |2013年第5期|3853-3866|共14页
  • 作者

    P. Pavon-Dominguez; F. J. Jimenez-Hornero; E. Gutierrez He Rave;

  • 作者单位

    Department of Graphics Engineering and Geomatics,University of Cordoba,Gregor Mendel Building (3rd floor), Campus Rabanales,14071 Cordova, Spain;

    Department of Graphics Engineering and Geomatics,University of Cordoba,Gregor Mendel Building (3rd floor), Campus Rabanales,14071 Cordova, Spain;

    Department of Graphics Engineering and Geomatics,University of Cordoba,Gregor Mendel Building (3rd floor), Campus Rabanales,14071 Cordova, Spain;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);美国《化学文摘》(CA);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    ozone forecasting; multiple linear regression; scaling; multifractal;

    机译：臭氧预报;多元线性回归;缩放多重分形;

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