Air Quality Forecasting with Machine Learning Models

Description

This repository contains the refactored and cleaned-up versions of machine learning models originally developed for the paper: Air Quality Forecasting with Machine Learning. The original version of the models is available at GitLab Repository.

The primary focus of this repository is to provide well-documented, standardized, and improved versions of these models for better usability and understanding.

Features

• Refactored Code: The original codebase from the GitLab repository has been significantly refactored for better readability and maintainability.
• Documentation: Detailed comments and descriptions have been added to improve clarity.

Models

This repository includes the following models:

• Model A: Random forecast classifiers and multiple linear regression models.
• Model B: Two-phase random forest regression model.

Getting Started

Dependencies

ML_env.yml contains the Python libraries needed to run these models.

Installing

To set up the project, run the following commands:

git clone git@github.com:yunhal/ML_air_quality_forecast.git
cd ML_air_quality_forecast
conda env create -f ML_env.yml

Executing Program:

• To prepare the input data for machine learning:

python prep_input_data.py

*To compute O3/PM2.5 predictions using the input datasets:

python Predict_O3_PM25.py

Main Authors:

• Kai Fan
• Yunha Lee (PI)