zlxy9892/ST-CausalConvNet
A spatiotemporal causal convolutional network for predicting PM2.5 concentrations.
This tool helps environmental scientists and urban planners forecast hourly PM2.5 concentrations, which are tiny, harmful airborne particles. By analyzing historical air quality data across an area, it provides predictions that can inform public health advisories and pollution control strategies. This is designed for researchers and practitioners focused on air quality management.
No commits in the last 6 months.
Use this if you need to predict future hourly PM2.5 concentrations based on past spatiotemporal data patterns for a specific region.
Not ideal if you are looking to predict other air pollutants, or if you need real-time, instantaneous readings rather than forecasts.
Stars
36
Forks
11
Language
Python
License
Apache-2.0
Category
Last pushed
Jul 25, 2022
Commits (30d)
0
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