INTELLIGENT SYSTEM FOR THE PLACEMENT OF URBAN ENVIRONMENTAL MONITORING STATIONS
DOI:
https://doi.org/10.28925/2663-4023.2025.27.728Keywords:
information technology, smart city, infrastructure, ecological monitoring, environment, air quality analysisAbstract
The article emphasizes the key role of information systems in the development of the environmental component of modern smart cities. It highlights that the implementation of IoT and Data Science technologies can significantly enhance the efficiency of monitoring and managing environmental parameters, which in turn allows for timely detection and response to environmental issues. This approach, combined with the use of intelligent information systems, can facilitate the prompt adoption of effective measures to eliminate and minimize negative impacts on the environment. A review of previous studies dedicated to the application of various information technologies and systems for optimizing the placement of resources, such as weather stations and environmental monitoring sensors, is conducted. It is substantiated that while linear and nonlinear methods provide accurate solutions, they require significant computational resources. Evolutionary algorithms offer flexibility, but their results can be less predictable. It is noted that evolutionary algorithms, such as genetic algorithms, demonstrate significant potential in solving tasks related to large data volumes. Machine learning methods can detect hidden patterns but require large volumes of data for training. Network algorithms optimize the placement of stations considering network topology, although their implementation can be complex. The conclusion emphasizes the need for further research to select optimal algorithms for solving tasks related to the placement of monitoring stations in cities, taking into account the specific requirements of each situation. This opens up prospects for developing optimization methods for air quality monitoring station networks in smart cities. The need for further research to improve optimization methods for monitoring station networks in urban conditions is underscored.
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