Combining deep learning and machine learning techniques to track air pollution in relation to vegetation cover utilizing remotely sensed data.
Journal:
Journal of environmental management
PMID:
39914214
Abstract
The rapid urban expansion in Dhaka, the capital of Bangladesh, has escalated air pollution levels and led to a significant decrease in green spaces. This study employed machine learning (ML) and deep learning (DL) techniques to examine the relationship between rising concentrations of particulate matter (PM2.5 and PM10) and decreasing urban green spaces from 1990 to 2022. The ML algorithms, specifically XGB, SVM, and RF, effectively predicted high air pollution areas, while DL models Unet, Unet++, MAnet, and Linknet accurately forecasted vegetation cover trends. The findings confirm a strong negative correlation between increased air pollution and vegetation. The decline in green spaces is not only a local concern but also has broader regional implications due to the transboundary nature of air pollution. The results highlight the critical need for pollution management strategies and urban planning that prioritize green infrastructure. The study also emphasizes the value of using ML and DL techniques for accurate, data-driven environmental assessments and predictions. Future studies could incorporate high-resolution images and integrate socioeconomic data to achieve a more comprehensive perspective on the urban environmental challenges faced by rapidly developing cities like Dhaka. The use of an integrated ML and DL strategy as highlighted in this research appears to be a practical and economical method for tracking vegetation degradation and change, and in establishing the causal links to air pollution.