A new model of air quality prediction using lightweight machine learning
Air pollution has become one of the environmental concerns in recent years due to its harmful threats to human health. To inform people about the air quality in their living areas, it is essential to measure the extent of pollution in the atmosphere. Air pollution sensors are assembled at static, fi...
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| Định dạng: | Bài trích |
| Ngôn ngữ: | English |
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Springer
2022
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| Truy cập trực tuyến: | https://link.springer.com/article/10.1007/s13762-022-04185-w https://dlib.phenikaa-uni.edu.vn/handle/PNK/5966 https://doi.org/10.1007/s13762-022-04185-w |
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oai:localhost:PNK-59662022-08-17T05:54:51Z A new model of air quality prediction using lightweight machine learning N. H. Van P. Van Thanh D. N. Tran D. T. Tran Air Quality Index Lightweight machine learning Air pollution has become one of the environmental concerns in recent years due to its harmful threats to human health. To inform people about the air quality in their living areas, it is essential to measure the extent of pollution in the atmosphere. Air pollution sensors are assembled at static, fixed-site measurement monitoring stations to acquire data. The data can be processed at the fixed stations or transmitted to the server to predict the Air Quality Index (AQI). Some previous studies applied machine learning algorithms to predict the AQI. Even though those works showed good performance on specific data, the results are not consistent on different datasets 2022-07-13T02:00:08Z 2022-07-13T02:00:08Z 2022 Bài trích https://link.springer.com/article/10.1007/s13762-022-04185-w https://dlib.phenikaa-uni.edu.vn/handle/PNK/5966 https://doi.org/10.1007/s13762-022-04185-w en Springer |
| institution |
Digital Phenikaa |
| collection |
Digital Phenikaa |
| language |
English |
| topic |
Air Quality Index Lightweight machine learning |
| spellingShingle |
Air Quality Index Lightweight machine learning N. H. Van P. Van Thanh D. N. Tran D. T. Tran A new model of air quality prediction using lightweight machine learning |
| description |
Air pollution has become one of the environmental concerns in recent years due to its harmful threats to human health. To inform people about the air quality in their living areas, it is essential to measure the extent of pollution in the atmosphere. Air pollution sensors are assembled at static, fixed-site measurement monitoring stations to acquire data. The data can be processed at the fixed stations or transmitted to the server to predict the Air Quality Index (AQI). Some previous studies applied machine learning algorithms to predict the AQI. Even though those works showed good performance on specific data, the results are not consistent on different datasets |
| format |
Bài trích |
| author |
N. H. Van P. Van Thanh D. N. Tran D. T. Tran |
| author_facet |
N. H. Van P. Van Thanh D. N. Tran D. T. Tran |
| author_sort |
N. H. Van |
| title |
A new model of air quality prediction using lightweight machine learning |
| title_short |
A new model of air quality prediction using lightweight machine learning |
| title_full |
A new model of air quality prediction using lightweight machine learning |
| title_fullStr |
A new model of air quality prediction using lightweight machine learning |
| title_full_unstemmed |
A new model of air quality prediction using lightweight machine learning |
| title_sort |
new model of air quality prediction using lightweight machine learning |
| publisher |
Springer |
| publishDate |
2022 |
| url |
https://link.springer.com/article/10.1007/s13762-022-04185-w https://dlib.phenikaa-uni.edu.vn/handle/PNK/5966 https://doi.org/10.1007/s13762-022-04185-w |
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1751856319807094784 |
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8.891145 |