Air Quality Prediction in Beijing: Machine and Deep Learning Analysis
In densely populated urban hubs like Beijing, the presence of PM2.5, a critical air quality metric, poses significant hazards to human health and the environment. This study delves into predictive modeling approaches for forecasting PM2.5 concentrations in response to escalating concerns.We analyze...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
EDP Sciences
2024-01-01
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| Series: | ITM Web of Conferences |
| Subjects: | |
| Online Access: | https://www.itm-conferences.org/articles/itmconf/pdf/2024/11/itmconf_icaetm2024_01012.pdf |
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| Summary: | In densely populated urban hubs like Beijing, the presence of PM2.5, a critical air quality metric, poses significant hazards to human health and the environment. This study delves into predictive modeling approaches for forecasting PM2.5 concentrations in response to escalating concerns.We analyze a wide range of approaches, including RDF, CNN, STM and fundamental statistical techniques, by closely analyzing Beijing’s PM2.5 concentrations and historical meteorological information. According to our research, CNN outperforms LSTM and shows excellent accuracy in forecasting PM2.5 levels. While random forest and linear regression exhibit competitiveness, their predictive prowess falls short in comparison. Conversely, less precise statistical techniques reliant on mean pollution levels are employed. The research outcomes offer significant insights to environmental authorities and policymakers regarding the effectiveness of various predictive modeling strategies for PM2.5 forecasting. Particularly noteworthy are the remarkable capabilities of deep learning techniques, notably CNN and LSTM, in discerning intricate correlations within environmental datasets. These strategies have the potential to address the urgent problem of air pollution and lessen its detrimental impact on both human health and the environment by increasing the precision of air quality predictions. |
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| ISSN: | 2271-2097 |