| 融合类Stacking算法的杭州臭氧浓度预测 |
| 摘要点击 2071 全文点击 697 投稿时间：2023-10-28 修订日期：2023-12-08 |
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| 中文关键词 类Stacking算法 轻量级梯度提升机（LightGBM）算法 长短期记忆模型（LSTM） Informer模型 普通最小二乘法（OLS） |
| 英文关键词 class Stacking algorithm light gradient boosting machine（LightGBM） algorithm long short-term memory model（LSTM） Informer model ordinary least squares（OLS） |
| DOI 10.13227/j.hjkx.20240919 |
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| 中文摘要 |
| 针对目前单机器学习模型对臭氧日均浓度预测精度较低的问题,提出一种融合类Stacking算法的臭氧浓度预测方法（FSOP）,将统计方法普通最小二乘法（OLS）与机器学习算法相融合,通过集成不同学习器的优势来提高臭氧浓度预测模型的预测精度. 采用杭州市2017年1月至2022年12月臭氧日最大8h浓度平均值的观测数据和气象再分析数据,根据Stacking算法的原理,先分别建立基于轻量级梯度提升机（LightGBM）算法、 长短期记忆模型（LSTM）和Informer模型的特定臭氧浓度预测模型,再将以上模型的预测结果作为元特征,利用OLS算法获取臭氧浓度的预测表达式对臭氧浓度观测值进行拟合. 结果表明,融合类Stacking算法后的模型预测精度获得提升,臭氧浓度拟合效果更好. 其中,R2、 RMSE和MAE分别为0.84、 19.65 μg·m-3和15.50 μg·m-3,较单个机器学习模型预测精度提升了8%左右. |
| 英文摘要 |
| Aiming at the problem that the single machine learning model has low prediction accuracy of daily average ozone concentration, an ozone concentration prediction method based on the fusion class Stacking algorithm （FSOP） was proposed, which combined the statistical method ordinary least squares （OLS） with machine learning algorithms and improved the prediction accuracy of the ozone concentration prediction model by integrating the advantages of different learners. Based on the principle of the Stacking algorithm, the observation data of the daily maximum 8h ozone average concentration and meteorological reanalysis data in Hangzhou from January 2017 to December 2022 were used. Firstly, the specific ozone concentration prediction models based on the light gradient boosting machine （LightGBM） algorithm, long short-term memory model （LSTM）, and Informer model were established, respectively. Then, the prediction results of the above models were used as meta-features, and the OLS algorithm was used to obtain the prediction expression of ozone concentration to fit the observed ozone concentration. The results showed that the prediction accuracy of the model combined with the class Stacking algorithm was improved, and the fitting effect of ozone concentration was better. Among them, R2, RMSE, and MAE were 0.84, 19.65 μg·m-3, and 15.50 μg·m-3, respectively, which improved the prediction accuracy by approximately 8% compared with that of the single machine learning model. |
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