leizhang-geo/ST-CausalConvNet
A spatiotemporal causal convolutional network for predicting air pollution.
This project helps environmental scientists and urban planners forecast hourly PM2.5 concentrations in specific urban areas, like Beijing. It takes historical air quality data from multiple monitoring stations as input and provides predictions for future PM2.5 levels. The end-user persona is typically an environmental researcher or urban policy analyst focused on air quality management.
No commits in the last 6 months.
Use this if you need to accurately predict hourly PM2.5 air pollution using spatiotemporal data from a network of monitoring stations.
Not ideal if you are looking to predict other types of environmental pollutants, or if your primary interest is in global climate modeling rather than localized urban air quality.
Stars
72
Forks
15
Language
Python
License
Apache-2.0
Category
Last pushed
Jul 25, 2022
Commits (30d)
0
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