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• W4313444864 abstract "According to the WHO, 50% of the global population is exposed to impure air. Air pollution levels are estimated using an air quality index (AQI). The AQI value is computed using pollutant information captured by specialised equipment installed in different regions. Machine learning is widely used in the estimation process without considering the regional characteristics that also influence the levels of pollution. Capturing pollutant and meteorological data in rural regions is challenging with the limited installation of air quality machines. Air pollution estimation is a complex and intricate process. In the proposed work, a deep learning model is initially employed to classify a region as urban or rural. The sources of pollution are impacted by the region, such information is critical in the overall estimation of an AQI. Various deep learning models are evaluated, and VGG-16 architecture is preferred for the process of region classification. Satellite images can be used for analysis, and the model generates an accuracy 99.75%. The output of this classification process is further passed on the AQI random forest model, which estimates the AQI using pollutant, meteorological and region information. The ensemble model generates an accuracy of 81%." @default.
• W4313444864 created "2023-01-06" @default.
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• W4313444864 date "2023-01-01" @default.
• W4313444864 modified "2023-09-29" @default.
• W4313444864 title "Region Classification for Air Quality Estimation Using Deep Learning and Machine Learning Approach" @default.
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• W4313444864 doi "https://doi.org/10.1007/978-981-19-5868-7_25" @default.
• W4313444864 hasPublicationYear "2023" @default.
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