Prediction of PM10 concentration in Seoul, Korea using Bayesian network

Minjoo Jo; Rosy Oh; Man Suk Oh

doi:10.29220/CSAM.2023.30.5.517

Prediction of PM10 concentration in Seoul, Korea using Bayesian network

Minjoo Jo, Rosy Oh, Man Suk Oh

Department of Statistics

Research output: Contribution to journal › Article › peer-review

Abstract

Recent studies revealed that fine dust in ambient air may cause various health problems such as respiratory diseases and cancer. To prevent the toxic effects of fine dust, it is important to predict the concentration of fine dust in advance and to identify factors that are closely related to fine dust. In this study, we developed a Bayesian network model for predicting PM10 concentration in Seoul, Korea, and visualized the relationship between important factors. The network was trained by using air quality and meteorological data collected in Seoul between 2018 and 2021. The study results showed that current PM10 concentration, season, carbon monoxide (CO) were the top 3 effective factors in 24 hours ahead prediction of PM10 concentration in Seoul, and that there were interactive effects.

Original language	English
Pages (from-to)	517-530
Number of pages	14
Journal	Communications for Statistical Applications and Methods
Volume	30
Issue number	5
DOIs	https://doi.org/10.29220/CSAM.2023.30.5.517
State	Published - 2023

Bibliographical note

Publisher Copyright:
© 2023 The Korean Statistical Society, and Korean International Statistical Society. All rights reserved.

Keywords

air pollution
Bayesian network
machine learning
PM10
prediction

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.29220/CSAM.2023.30.5.517

Cite this

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title = "Prediction of PM10 concentration in Seoul, Korea using Bayesian network",

abstract = "Recent studies revealed that fine dust in ambient air may cause various health problems such as respiratory diseases and cancer. To prevent the toxic effects of fine dust, it is important to predict the concentration of fine dust in advance and to identify factors that are closely related to fine dust. In this study, we developed a Bayesian network model for predicting PM10 concentration in Seoul, Korea, and visualized the relationship between important factors. The network was trained by using air quality and meteorological data collected in Seoul between 2018 and 2021. The study results showed that current PM10 concentration, season, carbon monoxide (CO) were the top 3 effective factors in 24 hours ahead prediction of PM10 concentration in Seoul, and that there were interactive effects.",

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AU - Jo, Minjoo

AU - Oh, Rosy

AU - Oh, Man Suk

PY - 2023

Y1 - 2023

N2 - Recent studies revealed that fine dust in ambient air may cause various health problems such as respiratory diseases and cancer. To prevent the toxic effects of fine dust, it is important to predict the concentration of fine dust in advance and to identify factors that are closely related to fine dust. In this study, we developed a Bayesian network model for predicting PM10 concentration in Seoul, Korea, and visualized the relationship between important factors. The network was trained by using air quality and meteorological data collected in Seoul between 2018 and 2021. The study results showed that current PM10 concentration, season, carbon monoxide (CO) were the top 3 effective factors in 24 hours ahead prediction of PM10 concentration in Seoul, and that there were interactive effects.

AB - Recent studies revealed that fine dust in ambient air may cause various health problems such as respiratory diseases and cancer. To prevent the toxic effects of fine dust, it is important to predict the concentration of fine dust in advance and to identify factors that are closely related to fine dust. In this study, we developed a Bayesian network model for predicting PM10 concentration in Seoul, Korea, and visualized the relationship between important factors. The network was trained by using air quality and meteorological data collected in Seoul between 2018 and 2021. The study results showed that current PM10 concentration, season, carbon monoxide (CO) were the top 3 effective factors in 24 hours ahead prediction of PM10 concentration in Seoul, and that there were interactive effects.

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KW - Bayesian network

KW - machine learning

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VL - 30

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JO - Communications for Statistical Applications and Methods

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Prediction of PM10 concentration in Seoul, Korea using Bayesian network

Abstract

Bibliographical note

Keywords

UN SDGs

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