2024
DOI: 10.21203/rs.3.rs-3912524/v1
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Spatiotemporal analysis of PM2.5 estimated using machine learning over Greater Bangkok: Variability, trends, and persistence

Nishit Aman,
Sirima Panyametheekul,
Ittipol Pawarmart
et al.

Abstract: The estimation of surface PM2.5 over Greater Bangkok (GBK) was done using six individual machine learning models (random forest, adaptive boosting, gradient boosting, extreme gradient boosting, light gradient boosting, and cat boosting), and a stacked ensemble model (SEM) during the dry season (November–April) for 2018–2022. The predictor variables include aerosol optical depth (AOD) from the Himawari-8 satellite, a set of meteorological variables from ERA5_LAND and ERA5 reanalysis datasets, fire hotspots coun… Show more

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