基于时空优化模型的PM2.5遥感估测研究
PM2.5 remote sensing estimation based on spatiotemporal factor optimization model
张娜 1陈文倩 1白雪松 1曹肖奕2
作者信息
- 1. 青岛理工大学信息与控制学院,山东 青岛 266520
- 2. 兰州大学大气科学学院,半干旱气候变化教育部重点实验室,甘肃 兰州 730000
- 折叠
摘要
为了获得连续的PM2.5浓度时空分布并提高估算精度,提出了一种新的基于时空因子优化的PM2.5估测模型(SFRF).SFRF模型是时空因子通过卷积特征融合到随机森林算法(RF)体系中,通过集成高分辨率(1km)卫星反演的气溶胶光学厚度(AOD)产品以及气象数据、夜间灯光数据和植被数据构建SFRF模型来进行对2019年的山东省地区PM2.5浓度的准确预测,生成山东省高空间分辨率(1km)的PM2.5浓度.采用十折交叉验证法,评估了SFRF模型的性能,并与BPNN、SVM、XGBoost、RF、PCA-RF模型进行对比.结果表明:SFRF模型验证的决定系数和均方根误差(RMSE)值分别为0.85和8.10 µg/m3,优于其他模型.SFRF模型可以在日、季、年尺度上以较高的空间分辨率来估测山东省PM2.5浓度.
Abstract
In order to obtain the continuous spatiotemporal distribution of PM2.5 concentration and improve the estimation accuracy,this paper proposes a new PM2.5 estimation model(SFRF)based on the optimization of spatiotemporal factors.The SFRF model integrates spatiotemporal factors into a random forest(RF)algorithm by integrating high-resolution(1km)satellite-retrieved aerosol optical depth(AOD)products,as well as meteorological data,nighttime light data,and vegetation.Using these data to build an SFRF model to accurately predict the PM2.5 concentration in Shandong Province in 2019 and generate high spatial resolution(1km)PM2.5 concentration in Shandong Province.The performance of the SFRF model was evaluated using the ten-fold cross-validation method and compared with the BPNN,SVM,XGBoost,RF and PCA-RF models.The results showed that the coefficient of determination and root mean square error(RMSE)values of the SFRF model verification are 0.85 and 8.10µg/m3,respectively,which are better than other models.The SFRF model can estimate PM2.5 concentration in Shandong Province with high spatial resolution on daily,seasonal,and annual scales.
关键词
PM2.5/时空优化模型/AOD/山东省地区Key words
PM2.5/spatiotemporal factor optimization model(SFRF)/AOD/Shandong province area引用本文复制引用
基金项目
山东省自然科学青年基金项目(ZR2023QD070)
十三五和十四五"一三五"规划任务管理基金项目(B2-2023-0239)
山东省重点研发计划项目(2023RZA02017)
出版年
2024
NETL
NSTL
万方数据