zouguojian/RCL-Learning
In this study, we propose an end-to-end deep learning model-RCL-Learning that integrates ResNet and ConvLSTM.
This project helps environmental agencies and public health organizations forecast airborne particulate matter (PM2.5) levels. It takes current and historical air quality readings and meteorological data from various cities as input. The output is an accurate prediction of future PM2.5 concentrations for a target city, helping with early warning systems and pollution control planning.
No commits in the last 6 months.
Use this if you need to predict PM2.5 concentrations for a target city, especially for short-term forecasts (1-15 hours out) using spatiotemporal data.
Not ideal if your primary goal is long-term climate modeling or predicting a wide range of different air pollutants beyond PM2.5.
Stars
18
Forks
1
Language
Python
License
MIT
Category
Last pushed
Jun 25, 2022
Commits (30d)
0
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