| 基于多机器学习集合算法的吉林省空气污染预报研究 |
| Research on Air Pollution Forecast for Jilin Province Based on Multi-Machine Learning Ensemble Algorithm |
| 投稿时间：2025-01-07 修订日期：2025-11-03 |
| DOI：10.19316/j.issn.1002-6002.2026.02.11 |
| 中文关键词: 空气污染 机器学习 集合预报 |
| 英文关键词:air pollution machine learning ensemble forecast |
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| 中文摘要: |
| 基于气象预报与污染观测数据,评估了梯度提升树(XGB)和深度神经网络(DNN)算法在吉林省空气质量预测中的性能。2023年的预报评估结果表明,XGB模型在污染物浓度预测方面展现出优异的趋势拟合能力,对细颗粒物(PM2.5)与臭氧(O3)浓度预测的相关系数多高于0.78和0.83,AQI预测范围准确率达40%~59%,且对高浓度事件捕捉能力突出,但在污染平稳时段的误差控制不足。DNN模型虽能反映污染物浓度的总体趋势,但整体性能相对逊色,且存在峰值低估和结果稳定性差的问题。为此,引入融合上述2种算法优势的优化集合算法(OCF)进行集合预报,结果表明,OCF显著提升了预测的稳定性与精度,在低浓度污染情形和平稳时段的校正效果尤为显著。相较于XGB模型,OCF通过集合策略使PM2.5和O3的平均预测偏差分别降低1.4 μg/m3和7.1 μg/m3。XGB模型在多数区域表现卓越,而OCF则展现出更强的稳健性,为吉林省精细化空气质量定量预报提供了多维度的技术支撑。 |
| 英文摘要: |
| This study evaluated the performance of Gradient Boosting Trees (XGB) and Deep Neural Networks (DNN) algorithms for air quality prediction in Jilin Province using meteorological forecasts and pollution observation data.The forecast evaluation results for 2023 demonstrated that the XGB model exhibited excellent trend-fitting capability in predicting pollutant concentrations.Its correlation coefficients for PM2.5 and ozone concentration predictions generally exceeded 0.78 and 0.83,respectively,and the accuracy rate of AQI prediction ranges reached 40%-59%.It effectively captured high-concentration events but exhibited limitations in error control during stable pollution periods.While the DNN model captured the overall pollutant concentration trends,its overall performance was inferior,characterized by peak underestimation and unstable results.To address these limitations,the Optimized Combination Forecasting (OCF) algorithm was introduced to integrate the strengths of both models.The results indicated that OCF significantly enhanced prediction stability and accuracy,particularly during low-concentration pollution and stable periods.Compared to the standalone XGB model,OCF reduced the mean prediction bias for PM2.5 and ozone by 1.4 μg/m3 and 7.1 μg/m3,respectively,through its ensemble strategy.In conclusion,while XGB delivered superior performance in most areas,while OCF demonstrated greater robustness,providing multi-dimensional technical support for refined quantitative air quality forecasting in Jilin Province. |
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