Urban ambient air quality data mining and visualisation
Air quality data analysis is based on real-time data collection, and how to use them for prediction after obtaining a large amount of data is an important problem to be solved in air quality prediction. The aim of this paper is to study urban ambient air quality data mining and visualisation. The co...
| Main Authors: | , , |
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| Format: | Conference or Workshop Item |
| Published: |
IEEE
2022
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| Online Access: | http://psasir.upm.edu.my/id/eprint/37623/ |
| _version_ | 1848848657219059712 |
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| author | Lyu, Linjie Kong, Jingyi Peng, Yingyi |
| author_facet | Lyu, Linjie Kong, Jingyi Peng, Yingyi |
| author_sort | Lyu, Linjie |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | Air quality data analysis is based on real-time data collection, and how to use them for prediction after obtaining a large amount of data is an important problem to be solved in air quality prediction. The aim of this paper is to study urban ambient air quality data mining and visualisation. The concepts related to information visualisation, data mining and exponential smoothing methods are described. The architecture of the data mining system for urban ambient air quality in this paper is proposed. Taking city M as an example, an ambient air quality data warehouse is established and an exponential smoothing technique is used to design a prediction model. The exponential smoothing method was used to predict the medium and long-term ambient air quality in the ambient air quality data mining system. The experiments showed that the prediction model had good prediction accuracy. |
| first_indexed | 2025-11-15T09:37:59Z |
| format | Conference or Workshop Item |
| id | upm-37623 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-15T09:37:59Z |
| publishDate | 2022 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-376232023-10-05T01:22:49Z http://psasir.upm.edu.my/id/eprint/37623/ Urban ambient air quality data mining and visualisation Lyu, Linjie Kong, Jingyi Peng, Yingyi Air quality data analysis is based on real-time data collection, and how to use them for prediction after obtaining a large amount of data is an important problem to be solved in air quality prediction. The aim of this paper is to study urban ambient air quality data mining and visualisation. The concepts related to information visualisation, data mining and exponential smoothing methods are described. The architecture of the data mining system for urban ambient air quality in this paper is proposed. Taking city M as an example, an ambient air quality data warehouse is established and an exponential smoothing technique is used to design a prediction model. The exponential smoothing method was used to predict the medium and long-term ambient air quality in the ambient air quality data mining system. The experiments showed that the prediction model had good prediction accuracy. IEEE 2022 Conference or Workshop Item PeerReviewed Lyu, Linjie and Kong, Jingyi and Peng, Yingyi (2022) Urban ambient air quality data mining and visualisation. In: 2022 International Conference on Artificial Intelligence of Things and Crowdsensing (AIoTCs), 26-28 Oct. 2022, Nicosia, Cyprus. (pp. 616-620). https://ieeexplore.ieee.org/document/10102166 10.1109/AIoTCs58181.2022.00101 |
| spellingShingle | Lyu, Linjie Kong, Jingyi Peng, Yingyi Urban ambient air quality data mining and visualisation |
| title | Urban ambient air quality data mining and visualisation |
| title_full | Urban ambient air quality data mining and visualisation |
| title_fullStr | Urban ambient air quality data mining and visualisation |
| title_full_unstemmed | Urban ambient air quality data mining and visualisation |
| title_short | Urban ambient air quality data mining and visualisation |
| title_sort | urban ambient air quality data mining and visualisation |
| url | http://psasir.upm.edu.my/id/eprint/37623/ http://psasir.upm.edu.my/id/eprint/37623/ http://psasir.upm.edu.my/id/eprint/37623/ |