论文标题
使用MAIAC AOD数据，用于伊朗德黑兰的基于机器学习的框架，用于PM2.5的高分辨率映射
A machine learning-based framework for high resolution mapping of PM2.5 in Tehran, Iran, using MAIAC AOD data
论文作者
论文摘要
本文研究了使用高分辨率卫星AOD（MAIAIA）检索对PM2.5浓度高分辨率映射的可能性。为此，一个包括三个主要阶段的框架，即数据预处理；回归建模；并提出了模型部署。该框架的输出是一种机器学习模型，该模型训练有素，可以从Maiac AOD检索和气象数据中预测PM2.5。模型测试的结果揭示了开发的框架在PM2.5的高分辨率映射中的效率和能力，这在以前在城市进行的调查中没有实现。因此，这项研究每天首次实现，在R2约0.74和RMSE的Tehran中，PM2.5的1 km分辨率映射高于9.0 mg/m3。 关键字：Maiac； modis; aod;机器学习；深度学习； PM2.5;回归
This paper investigates the possibility of high resolution mapping of PM2.5 concentration over Tehran city using high resolution satellite AOD (MAIAC) retrievals. For this purpose, a framework including three main stages, data preprocessing; regression modeling; and model deployment was proposed. The output of the framework was a machine learning model trained to predict PM2.5 from MAIAC AOD retrievals and meteorological data. The results of model testing revealed the efficiency and capability of the developed framework for high resolution mapping of PM2.5, which was not realized in former investigations performed over the city. Thus, this study, for the first time, realized daily, 1 km resolution mapping of PM2.5 in Tehran with R2 around 0.74 and RMSE better than 9.0 mg/m3. Keywords: MAIAC; MODIS; AOD; Machine learning; Deep learning; PM2.5; Regression
