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Top2Vec Topic Modeling to Analyze the Dynamics of Publication Activity Related to Environmental Monitoring Using Unmanned Aerial Vehicles

Author

Listed:
  • Vladimir Albrekht

    (Institute of Automation and Information Technologies, Satbayev University (KazNRTU), 22 Satpayev Street, Almaty 050013, Kazakhstan)

  • Ravil I. Mukhamediev

    (Institute of Automation and Information Technologies, Satbayev University (KazNRTU), 22 Satpayev Street, Almaty 050013, Kazakhstan
    Institute of Information and Computational Technologies, Almaty 050010, Kazakhstan)

  • Yelena Popova

    (Transport and Management Department, Transport and Telecommunication Institute, Lauvas iela 2, LV-1003 Riga, Latvia)

  • Elena Muhamedijeva

    (Institute of Information and Computational Technologies, Almaty 050010, Kazakhstan)

  • Asset Botaibekov

    (Institute of Automation and Information Technologies, Satbayev University (KazNRTU), 22 Satpayev Street, Almaty 050013, Kazakhstan)

Abstract

Unmanned aerial vehicles (UAVs) play a key role in the process of contemporary environmental monitoring, enabling more frequent and detailed observations of various environmental parameters. With the rapid growth of scientific publications on this topic, it is important to identify the key trends and directions. This study uses the Top2Vec algorithm for topic modeling algorithm aimed at analyzing abstracts of more than 556 thousand scientific articles published on the arXiv platform from 2010 to 2023. The analysis was conducted in five key domains: air, water, and surface pollution monitoring; causes of pollution; and challenges in the use of UAVs. The research method included data collection and pre-processing, topic modeling, and quantitative analysis of publication activity using indicators of the rate (D1) and acceleration (D2) of change in the number of publications. The study allows concluding that the main challenge for the researchers is the task of processing data obtained in the course of monitoring. The second most important factor is the reduction in restrictions on the UAV flight duration. Among the causes of pollution, agricultural activities will be considered as a priority. Research in monitoring greenhouse gas emissions will be the most topical in air quality monitoring, while erosion and sedimentation—in the area of land surface control. Thermal pollution, microplastics, and chemical pollution are most relevant in the field of water quality control. On the other hand, the interest of the scientific community in topics related to soil pollution, particulate matter, sensor calibration, and volatile organic compounds is decreasing.

Suggested Citation

  • Vladimir Albrekht & Ravil I. Mukhamediev & Yelena Popova & Elena Muhamedijeva & Asset Botaibekov, 2025. "Top2Vec Topic Modeling to Analyze the Dynamics of Publication Activity Related to Environmental Monitoring Using Unmanned Aerial Vehicles," Publications, MDPI, vol. 13(2), pages 1-25, March.
    • Handle: RePEc:gam:jpubli:v:13:y:2025:i:2:p:15-:d:1620017
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    References listed on IDEAS

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    1. Daniel D. Lee & H. Sebastian Seung, 1999. "Learning the parts of objects by non-negative matrix factorization," Nature, Nature, vol. 401(6755), pages 788-791, October.
    2. Justyna Jońca & Marcin Pawnuk & Yaroslav Bezyk & Adalbert Arsen & Izabela Sówka, 2022. "Drone-Assisted Monitoring of Atmospheric Pollution—A Comprehensive Review," Sustainability, MDPI, vol. 14(18), pages 1-31, September.
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