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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...

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Main Authors: Lyu, Linjie, Kong, Jingyi, Peng, Yingyi
Format: Conference or Workshop Item
Published: IEEE 2022
Online Access:http://psasir.upm.edu.my/id/eprint/37623/
https://ieeexplore.ieee.org/document/10102166
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spelling my.upm.eprints.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
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
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.
format Conference or Workshop Item
author Lyu, Linjie
Kong, Jingyi
Peng, Yingyi
spellingShingle Lyu, Linjie
Kong, Jingyi
Peng, Yingyi
Urban ambient air quality data mining and visualisation
author_facet Lyu, Linjie
Kong, Jingyi
Peng, Yingyi
author_sort Lyu, Linjie
title Urban ambient air quality data mining and visualisation
title_short 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_sort urban ambient air quality data mining and visualisation
publisher IEEE
publishDate 2022
url http://psasir.upm.edu.my/id/eprint/37623/
https://ieeexplore.ieee.org/document/10102166
_version_ 1781706632446607360
score 13.211869

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