AIR QUALITY FORECASTING WITH THE SUPPORT OF MACHINE LEARNING

Liubomyr Monastyrskyi, V. Hura

Abstract


An information-analytical system for monitoring the atmospheric air of the city of Lviv has been developed. The system's overall architecture has been designed and implemented with software development. The system has been populated with data and successfully tested during data analysis to develop sections of the state monitoring program for air pollution control in the Lviv agglomeration.

The development process involved designing the overall architecture, including the components, modules, and their interconnections. Subsequently, the system was programmatically implemented, encompassing the development of necessary modules and functionalities. Data was collected from various sources such as air monitoring sensors and meteorological stations to populate the system.

Following the completion of system development, the system underwent testing and validation while addressing real-world analytical tasks. Specifically, the system was utilized in the development of sections of the state monitoring program for air pollution control in the Lviv agglomeration. This allowed for assessing the system's effectiveness and usefulness in addressing specific air quality management challenges.

Through these efforts, an information-analytical system for monitoring the atmospheric air of Lviv has been created, facilitating the collection, analysis, and utilization of vital information on air quality to improve the environmental situation and make informed decisions regarding air pollution control. Overall, the successful development and deployment of this information-analytical system for atmospheric air monitoring pave the way for a more sustainable and eco-friendly urban environment in Lviv and beyond.

Key words: prediction, analysis, air quality, ambient air, cross-platform development, software framework, air monitoring.


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DOI: http://dx.doi.org/10.30970/eli.22.6

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