Estimation of Ground PM2.5 Concentrations in Pakistan Using Convolutional Neural Network and Multi-Pollutant Satellite Images

نویسندگان

چکیده

During the last few decades, worsening air quality has been diagnosed in many cities around world. The accurately prediction of pollutants, particularly, particulate matter 2.5 (PM2.5) is extremely important for environmental management. A Convolutional Neural Network (CNN) P-CNN model presented this paper, which uses seven different pollutant satellite images, such as Aerosol index (AER AI), Methane (CH4), Carbon monoxide (CO), Formaldehyde (HCHO), Nitrogen dioxide (NO2), Ozone (O3) and Sulfur (SO2), auxiliary variables to estimate daily average PM2.5 concentrations. This study estimates concentrations various Pakistan (Islamabad, Lahore, Peshawar Karachi) by using images. dataset contains a total 2562 images from May-2019 April-2020. We compare analyze AlexNet, VGG16, ResNet50 on every dataset. accuracy machine learning models was checked with Mean Absolute Error (MAE), Root Square (RMSE) Percentage (MAPE). results show that more accurate than other approaches estimating presents robust useful

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ژورنال

عنوان ژورنال: Remote Sensing

سال: 2022

ISSN: ['2315-4632', '2315-4675']

DOI: https://doi.org/10.3390/rs14071735