A.V.T. AQ Analysis Using Entropy

Entropy analysis of air quality indicators using OpenAQ data sets to achieve the Goal 11 by UN's Sustainable Development Goals

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Inspiration

The project team has been developing mathematical models of the stability of processes and systems using entropy theory since 1996.

What it does

Entropy analysis of air quality indicators using OpenAQ data sets to achieve the Goal 11 by UN's Sustainable Development Goals

How we built it

In the SageMaker Studio Lab and JupiterLab development environments, a project was created in the Python 3 programming language to process open air quality datasets delivered by the OpenAQ project and AWS.

At the first stage of the analysis, the average daily concentration of solid particles in a given region was used to determine the maximum permissible norms in accordance with the WHO Air Quality Guideline Values link

In the second phase of the analysis of the original OpenAQ air quality datasets, entropy values were calculated.

Challenges we ran into

The choice of an effective access method and methods for analyzing the initial data sets.

Accomplishments that we're proud of

We have successfully applied the results of our scientific research for this project, carried out as part of the dissertation many years ago.

What we learned

Wonderful tools for the developer and researcher in the field of effective analysis of big data and machine learning, such as Sagemaker Studio Lab and Jupiter Lab

What's next for "A.V.T. AQ Analysis Using Entropy" Project

  1. Apply our ideas and an approach to creating an effective tool for specialists in the field of data analysis from subject areas
  2. Make the project to execute it more convenient to use, add graphs, forecast models, analyze more indicators
  3. Add the ability to analyze other data sets stored in AWS related to the topic of the project to ensure a more complex and accurate analysis.

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