Using deep ensemble forest for high-resolution mapping of PM2.5 from MODIS MAIAC AOD in Tehran, Iran.
Environ Monit Assess
; 195(3): 377, 2023 Feb 09.
Article
em En
| MEDLINE
| ID: mdl-36757448
ABSTRACT
High-resolution mapping of PM2.5 concentration over Tehran city is challenging because of the complicated behavior of numerous sources of pollution and the insufficient number of ground air quality monitoring stations. Alternatively, high-resolution satellite Aerosol Optical Depth (AOD) data can be employed for high-resolution mapping of PM2.5. For this purpose, different data-driven methods have been used in the literature. Recently, deep learning methods have demonstrated their ability to estimate PM2.5 from AOD data. However, these methods have several weaknesses in solving the problem of estimating PM2.5 from satellite AOD data. In this paper, the potential of the deep ensemble forest method for estimating the PM2.5 concentration from AOD data was evaluated. The results showed that the deep ensemble forest method with [Formula see text] gives a higher accuracy of PM2.5 estimation than deep learning methods ([Formula see text]) as well as classic data-driven methods such as random forest ([Formula see text]). Additionally, the estimated values of PM2.5 using the deep ensemble forest algorithm were used along with ground data to generate a high-resolution map of PM2.5. Evaluation of produced PM2.5 map revealed the good performance of the deep ensemble forest for modeling the variation of PM2.5 in the city of Tehran.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Poluentes Atmosféricos
/
Poluição do Ar
Tipo de estudo:
Prognostic_studies
País/Região como assunto:
Asia
Idioma:
En
Ano de publicação:
2023
Tipo de documento:
Article