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A Softsensor for Wind Measurements in Karst Caves.

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Area of Science:

  • Environmental Science
  • Meteorology
  • Data Science

Background:

  • Physical anemometers are commonly used for wind velocity measurements but can be fragile, power-intensive, or expensive.
  • Underground environments, such as caves, present unique challenges for deploying and maintaining physical sensors.
  • Data gaps in wind velocity measurements can occur due to sensor malfunction or inaccessibility.

Purpose of the Study:

  • To develop and evaluate a data-driven soft sensor for measuring wind velocity in cave passages.
  • To provide a viable alternative to physical anemometers for underground meteorology.
  • To assess the potential of soft sensors for filling data gaps or replacing physical sensors.

Main Methods:

  • A Gaussian process model was developed as a soft sensor.
  • The model was trained on one year of half-hourly wind velocity measurements from a cave passage.
  • Statistical measures and visual inspection were used to evaluate model performance on test data.

Main Results:

  • The developed Gaussian process model demonstrated good performance in measuring wind velocity.
  • Both selected model structures showed effective results, indicating robustness.
  • The soft sensor successfully captured wind velocity patterns in the cave environment.

Conclusions:

  • Data-driven soft sensors are a viable tool for underground meteorology.
  • Soft sensors can effectively measure wind velocity, offering an alternative to physical anemometers.
  • This technology can enhance data collection by filling gaps or replacing conventional sensors in challenging environments.