STAMATIC Time Series Analysis Project

Introduction

STAMATIC is a time series analysis project that aims to analyze and predict air pollutant levels in a coal mining region. The project focuses on the impact of coal open pit blasting on air quality and the environment. By leveraging time series data, this project aims to gain insights into pollutant levels during blasting periods and explore potential mitigation strategies for reducing air pollution during coal mining activities.

Features

The STAMATIC project offers the following features:

1. Data Collection: The project gathers real-time air quality data from various monitoring stations in the coal mining region. This data includes measurements of pollutants such as PM10, NO2, SO2, CO, ozone, benzene, and more.

2. Data Preprocessing: The collected time series data is preprocessed to handle missing values, outliers, and ensure data consistency. Time series specific preprocessing techniques like interpolation and resampling are employed to clean the data.

3. Data Visualization: STAMATIC provides interactive visualizations to explore pollutant trends over time. Users can analyze pollutant levels during blasting periods and observe correlations among different pollutants.

4. Time Series Analysis: The project employs various time series analysis techniques, including decomposition, differencing, and autocorrelation analysis, to understand underlying patterns and trends in the data.

5. Predictive Modeling: STAMATIC utilizes advanced predictive modeling techniques, such as linear interpolation, cubic interpolation, and spline interpolation, to forecast pollutant levels during blasting periods.

6. Mitigation Strategies: Based on the analysis and predictions, the project suggests mitigation strategies to control and reduce air pollution during coal mining activities. Emission control measures and cleaner technologies are explored as potential solutions.

Getting Started

To get started with the STAMATIC project, follow these steps:

1. Clone the repository to your local machine.

2. Install the required dependencies by running pip install -r requirements.txt.

3. Prepare the time series data by following the data preprocessing instructions in the data_preprocessing.ipynb notebook.

4. Explore the data visualizations and time series analysis techniques provided in the data_analysis.ipynb notebook.

5. Use the predictive models implemented in the predictive_models.ipynb notebook to forecast pollutant levels during blasting periods.

6. Refer to the project's documentation and wiki for additional information and resources.

Contribution Guidelines

Contributions to the STAMATIC project are welcome! If you find any bugs, have feature requests, or want to add improvements, feel free to create an issue or submit a pull request. Please follow the project's coding style and guidelines when contributing.

License

STAMATIC is released under the MIT License. You are free to use, modify, and distribute the code as per the terms of the license.

Acknowledgments

We would like to express our gratitude to all the contributors and researchers who have contributed to the field of time series analysis and air quality monitoring. Their work has been instrumental in the development of this project.

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With STAMATIC, we hope to contribute to a better understanding of air pollution during coal mining activities and provide valuable insights for environmental protection and sustainable mining practices.