Spatial prediction and visualization of PM2.5 susceptibility using machine learning optimization in a virtual reality environment
Particulate Matter (PM2.5)-based air pollution is a severe menace to health and the environment worldwide. Traditional methods for predicting and mapping PM2.5 are usually lagging due to the complexity and nonlinearity between different participating parameters. Moreover, there is also a significant...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2025-08-01
|
| Series: | International Journal of Digital Earth |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/17538947.2025.2513589 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|