| 基于随机森林模型的四川盆地臭氧污染预测 |
| 摘要点击 3221 全文点击 96 投稿时间：2023-04-25 修订日期：2023-07-07 |
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| 中文关键词 随机森林 臭氧污染 预测 四川盆地 气象因子 |
| 英文关键词 random forest ozone pollution prediction Sichuan Basin meteorological factors |
| 作者 | 单位 | E-mail | | 杨晓彤 | 国防科技大学气象海洋学院, 中国气象局高影响天气重点开放实验室, 长沙 410000 | yangxt115@hotmail.com | | 康平 | 成都信息工程大学大气科学学院, 高原大气与环境四川省重点实验室, 成都 610225 | kangping@cuit.edu.cn | | 王安怡 | 成都信息工程大学大气科学学院, 高原大气与环境四川省重点实验室, 成都 610225 | | | 臧增亮 | 国防科技大学气象海洋学院, 中国气象局高影响天气重点开放实验室, 长沙 410000 | | | 刘浪 | 国防科技大学气象海洋学院, 中国气象局高影响天气重点开放实验室, 长沙 410000 | |
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| 中文摘要 |
| 为研究四川盆地臭氧（O3）污染长期变化,使用四川盆地18个城市的地面O3浓度数据和气象观测数据,首先分析了2017~2020年间四川盆地O3浓度的时空分布特征,再利用随机森林模型,筛选出影响O3浓度变化的主导气象因子,构建了气象因子和O3浓度之间的统计预测模型,并对2020年四川盆地城市群的O3污染状况进行预测分析.结果表明：①2017~2020年间O3浓度呈现波动变化趋势,2019年出现一个低值,2020年O3浓度又有所回升.②气象影响因子中相对湿度、日最高温度和日照时数对O3浓度变化具有重要意义,而风速、气压和降水量的重要性较低；同时,气象因子之间也存在着不同的线性关系,气压与其他气象要素呈现负相关性,而剩余气象要素之间正相关关系较为明显.③基于随机森林构建的O3预测模型的拟合优度（R2）较高,展示出较好的预测性能,能够较好地预测O3浓度的长时间逐日变化,具有良好的稳定性和泛化能力.④通过对四川盆地18城市的O3浓度变化进行预测分析,结果表明除雅安外,所有城市预测模型的变量解释率均达到80%以上,说明随机森林模型能够较为准确地预测O3浓度的变化趋势. |
| 英文摘要 |
| To study the long-term variation in ozone (O3) pollution in Sichuan Basin,the spatiaotemporal distribution of O3 concentrations during 2017 to 2020 was analyzed using ground-level O3 concentration data and meteorological observation data from 18 cities in the basin. The dominant meteorological factors affecting the variation in O3 concentration were screened out,and a prediction model between meteorological factors and O3 concentration was constructed based on a random forest model. Finally,a prediction analysis of O3 pollution in the Sichuan Basin urban agglomeration during 2020 was carried out. The results showed that:① O3 concentrations displayed a fluctuating trend during the period from 2017 to 2020,with a downward trend in 2019 and a rebound in 2020. ② The fluctuating trend of O3 concentration was significantly influenced by relative humidity,daily maximum temperature,and sunshine hours,whereas wind speed,air pressure,and precipitation had less impact. The linear relationships between meteorological factors were different. Air pressure was negatively correlated with other meteorological factors,whereas the remaining meteorological factors had a positive correlation. ③ The goodness of fit statistics (R2) between the predicted and actual values of the O3 prediction model constructed based on random forest demonstrated a strong predictive performance and ability to accurately forecast the long-term daily variations in O3 concentration. The random forest O3 prediction model exhibited excellent stability and generalization capability. ④ The prediction analysis of O3 concentrations in 18 cities in the basin showed that the explanation rate of variables in the prediction model reached over 80% in all cities (except Ya'an),indicating that the random forest model predicted the trend of O3 concentration accurately. |
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