Analyzing and forecasting air pollution concentration in the capital and Southern Thailand using a lag-dependent Gaussian process model.
Air pollution
Forecast
Gaussian process
Machine learning
Particulate matter
Journal
Environmental monitoring and assessment
ISSN: 1573-2959
Titre abrégé: Environ Monit Assess
Pays: Netherlands
ID NLM: 8508350
Informations de publication
Date de publication:
26 Oct 2024
26 Oct 2024
Historique:
received:
19
05
2024
accepted:
16
10
2024
medline:
26
10
2024
pubmed:
26
10
2024
entrez:
25
10
2024
Statut:
epublish
Résumé
The air pollution problem has now amassed worldwide attention due to its multifaceted harm to human health. Exploring the concentration of air pollution and improving forecast have important consideration worldwide. In this research, we analyze the air pollution concentration of Southern Thailand and compare it with the central region. Also, we proposed a methodology based on the lag-dependent Gaussian process (LDGP), a Bayesian non-parametric machine learning model, with a stable optimization approach, which is a cluster-based multi-starter technique based on the Nelder-Mead optimizer. This model also provides the confidence band for forecasted values. We also used autoregressive deep neural network (AR-DNN), autoregressive random forest (AR-RF), gradient boosting (GB), and K-nearest neighbors (KNN) models. A comparison of the proposed methodology was performed on the daily air pollution data collected from the southern provinces and also from the capital of Thailand from 1 January 2018 to 31 December 2022. We used well-established performance evaluation measures to compare the performance of the models. To evaluate the bias due to overfit, we performed a tenfold cross-validation for all the pollutants in each region and compared the models to choose the best one. Moreover, we explored the concentration of air pollution in these regions. Results of descriptive analysis revealed that Bangkok had a much higher concentration of air pollution as compared to the southern region. However, the southern region had higher exposure to PM air pollutants as per WHO recommendations and also had higher exposure to O
Identifiants
pubmed: 39455462
doi: 10.1007/s10661-024-13275-w
pii: 10.1007/s10661-024-13275-w
doi:
Substances chimiques
Air Pollutants
0
Particulate Matter
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
1106Subventions
Organisme : Prince of Songkla University
ID : Postdoc Fellowship
Organisme : Ministry of Higher Education, Science, Research and Innovation, Thailand
ID : SAT6601245S
Informations de copyright
© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.
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