Normet for automated air quality intervention studies
Project description
normet
normet is a Python package to conduct automated data curation, automated machine learning-based meteorology/weather normalisation and causal analysis on air quality interventions for atmospheric science, air pollution and policy analysis. The main aim of this package is to provide a Swiss army knife enabling rapid automated-air quality intervention studies, and contributing to cross-disciplinary studies with public health, economics, policy, etc.
Installation
conda create -n normet python=3.9
conda activate normet
This package depends on AutoML from flaml. Install FLAML first:
conda install flaml -c conda-forge
Install normet from source:
git clone https://github.com/dsncas/normet.git
cd normet
python setup.py install
(optional) for jupyter notebook:
conda install jupyter
Main Features
Here are a few of the functions that normet implemented:
Automated data curation. Download air quality data and re-analysis data at any time in any area.
Automated machine learning. Help to select the ‘best’ ML model for the dataset and model training.
Partial dependency. Look at the drivers (both interactive and noninteractive) of changes in air pollutant concentrations and feature importance.
Weather normalisation. Decoupling emission-related air pollutant concentrations from meteorological effects.
Change point detection. Detect the change points caused by policy interventions.
Causal inference for air quality interventions. Attribution of changes in air pollutant concentrations to air quality policy interventions.
Documentation
You can find Demo and tutorials of the functions here.
Project details
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