Estimating visibility and understanding factors influencing its variations at Bangkok airport using machine learning and a game theory–based approach
Author:
Funder
Graduate School, Chulalongkorn University
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s11356-024-34548-4.pdf
Reference61 articles.
1. Aman N, Manomaiphiboon K, Pengchai P, Suwanathada P, Srichawana J, Assareh N (2019) Long-term observed visibility in eastern Thailand: temporal variation, association with air pollutants and weather factors, and trends. Atmosphere 10:122. https://doi.org/10.3390/atmos10030122
2. Aman N, Manomaiphiboon K, Pala-En N, Kokkaew E, Boonyoo T, Pattaramunikul S, Devkota B, Chotamonsak C (2020) Evolution of urban haze in Greater Bangkok and association with local weather and synoptic characteristics during two recent haze episodes. Int J Environ Res Public Health 17:9499. https://doi.org/10.3390/ijerph17249499
3. Aman N, Manomaiphiboon K, Suwattiga P, Assareh N, Limpaseni W, Suwanathada P, Soonsin V, Wang Y (2022) Visibility, aerosol optical depth, and low-visibility events in Bangkok during the dry season and associated local weather and synoptic patterns. Environ Monit Assess 194:322. https://doi.org/10.1007/s10661-022-09880-2
4. Aman N, Manomaiphiboon K, Pala-En N, Devkota B, Inerb M, Kokkaew E (2023) A study of urban haze and its association with cold surge and sea breeze for Greater Bangkok. Int J Environ Res Public Health 20:3482. https://doi.org/10.3390/ijerph20043482
5. Aman N, Manomaiphiboon K, Xian D, Gao L, Tian L, Pala-En N, Wang Y, Wangyao K (2024) Spatiotemporal estimation of hourly PM2.5 using AOD derived from geostationary satellite Fengyun-4A and machine learning models for Greater Bangkok. Air Qual Atmos Health 10:1–6. https://doi.org/10.1007/s11869-024-01524-3
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