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首页> 外文期刊>The Science of the Total Environment >A machine learning method to estimate PM_(2.5) concentrations across China with remote sensing, meteorological and land use information
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A machine learning method to estimate PM_(2.5) concentrations across China with remote sensing, meteorological and land use information

机译：一种利用遥感，气象和土地利用信息估算全国PM_（2.5）浓度的机器学习方法

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Machine learning algorithms have very high predictive ability. However, no study has used machine learning to estimate historical concentrations of PM2.5 (particulate matter with aerodynamic diameter ≤ 2.5 μm) at daily time scale in China at a national level.To estimate daily concentrations of PM2.5 across China during 2005-2016.Daily ground-level PM2.5 data were obtained from 1479 stations across China during 2014-2016. Data on aerosol optical depth (AOD), meteorological conditions and other predictors were downloaded. A random forests model (non-parametric machine learning algorithms) and two traditional regression models were developed to estimate ground-level PM2.5 concentrations. The best-fit model was then utilized to estimate the daily concentrations of PM2.5 across China with a resolution of 0.1° (≈10 km) during 2005-2016.The daily random forests model showed much higher predictive accuracy than the other two traditional regression models, explaining the majority of spatial variability in daily PM2.5 [10-fold cross-validation (CV) R2 = 83%, root mean squared prediction error (RMSE) = 28.1 μg/m3]. At the monthly and annual time-scale, the explained variability of average PM2.5 increased up to 86% (RMSE = 10.7 μg/m3 and 6.9 μg/m3, respectively).Taking advantage of a novel application of modeling framework and the most recent ground-level PM2.5 observations, the machine learning method showed higher predictive ability than previous studies.Random forests approach can be used to estimate historical exposure to PM2.5 in China with high accuracy.
机译：机器学习算法具有很高的预测能力。然而，尚无研究使用机器学习来估计全国范围内中国每日时间尺度上PM2.5（空气动力学直径≤2.5μm的颗粒物）的历史浓度.2005- 2016年.2014-2016年期间，每天从中国1479个站点获得了地面PM2.5每日数据。下载了有关气溶胶光学深度（AOD），气象条件和其他预测因子的数据。开发了一个随机森林模型（非参数机器学习算法）和两个传统回归模型来估计地面PM2.5浓度。然后利用最佳拟合模型估算2005-2016年中国PM2.5的日浓度，分辨率为0.1°（≈10km），日随机森林模型显示出比其他两种传统方法更高的预测准确性回归模型，解释了每日PM2.5的大部分空间变异性[10倍交叉验证（CV）R2 = 83％，均方根预测误差（RMSE）= 28.1μg/ m3]。在月度和年度时间尺度上，平均PM2.5的解释变异性增加了高达86％（RMSE = 10.7μg/ m3和6.9μg/ m3）。最近的地面PM2.5观测结果表明，机器学习方法比以前的研究具有更高的预测能力。随机森林方法可用于高精度估算中国PM2.5的历史暴露量。

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