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Abstract <p>The paper analyses the current state of the citizen science volunteer movement using numerous foreign and domestic sources. Key points of its development in several fields related to ecology and environmental sciences are highlighted. Special attention is paid to the observations of groundwater level, which is one of the important characteristics determining the process of streamflow formation. The point observations of this characteristic, whose capabilities are shown in the paper, was carried out by the volunteer movement on the Desna River for the period from 2020 to 2024 together with the observations of daily precipitation, atmospheric pressure, and air temperature at the nearest weather station. Using modern programming languages and technologies, the interrelationships of the measured characteristics are graphically analyzed in terms of the possibility of using this information in neural network technologies to develop methods for water regime forecasting.</p>

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Groundwater Level Monitoring in Developing the Citizen Science Volunteer Movement

  • 期刊论文

  • 发表日期：

    2025年7月1日

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  • A. V. Romanov,
  • V. O. Barinova,
  • V. A. Biryukova

摘要

Abstract

The paper analyses the current state of the citizen science volunteer movement using numerous foreign and domestic sources. Key points of its development in several fields related to ecology and environmental sciences are highlighted. Special attention is paid to the observations of groundwater level, which is one of the important characteristics determining the process of streamflow formation. The point observations of this characteristic, whose capabilities are shown in the paper, was carried out by the volunteer movement on the Desna River for the period from 2020 to 2024 together with the observations of daily precipitation, atmospheric pressure, and air temperature at the nearest weather station. Using modern programming languages and technologies, the interrelationships of the measured characteristics are graphically analyzed in terms of the possibility of using this information in neural network technologies to develop methods for water regime forecasting.