| 基于机器学习的珠三角秋季臭氧浓度预测 |
| 摘要点击 2236 全文点击 591 投稿时间：2023-02-07 修订日期：2023-03-13 |
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| 中文关键词 珠三角（PRD） 臭氧（O3） 日最大8 h浓度平均值（MDA8-O3） 机器学习 预测 |
| 英文关键词 Pearl River Delta （PRD） ozone （O3） daily maximum 8-hour average concentration（MDA8-O3） machine learning prediction |
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| 中文摘要 |
| 基于2015~2022年珠三角地区的臭氧（O3）日最大8 h浓度平均值[MDA8-O3,ρ（O3-8h）]的观测数据和气象再分析数据,运用支持向量回归（SVR）、随机森林（RF）、多层感知机（MLP）和轻量级梯度提升机（LG）这4种机器学习方法,建立MDA8-O3预测模型.结果表明,对于全年MDA8-O3预测而言,SVR模型的效果最好,决定系数（R2）达0.86,均方根误差（RMSE）和平均绝对误差（MAE）分别为16.3 μg·m-3和12.3 μg·m-3；对于秋季MDA8-O3预测而言,SVR模型的效果依然略优于LG和MLP,其R2、RMSE和MAE分别为0.88、19.8 μg·m-3和16.1 μg·m-3,RF模型在秋季的预测效果最差.采用全年数据构建的模型对秋季MDA8-O3的预测效果比仅采用秋季数据构建的模型效果好,R2相差0.08~0.14. |
| 英文摘要 |
| Based on the observation data of the daily maximum 8-hour ozone (O3) average concentration[MDA8-O3, ρ(O3-8h)] and meteorological reanalysis data in the Pearl River Delta Region from 2015 to 2022, four machine learning methods, i.e., support vector machine regression (SVR), random forest (RF), multi-layer perceptron (MLP), and lightweight gradient boosting machine (LG) were used to establish MDA8-O3 prediction models. The results showed that the SVR model had the best prediction performance on MDA8-O3 during the whole year, the coefficient of determination (R2) reached 0.86, and the root mean square error (RMSE) and mean absolute error (MAE) were 16.3 μg·m-3 and 12.3 μg·m-3, respectively. The prediction performance of the SVR model in autumn was still slightly better than that of LG and MLP, with R2,RMSE,and MAE values of 0.88, 19.8 μg·m-3,and 16.1 μg·m-3, respectively. The RF model performed the worst in the autumn prediction. In addition, the models trained by data from the whole year had better prediction ability on autumn MDA8-O3 than that of those only trained by autumn data, and the R2 differed 0.08-0.14. |
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