Abstract

Citizen science projects engage the public in monitoring the environment and can collect useful data. One example is the CrowdWater project, in which stream levels are observed and compared to reference photos taken at an earlier time to obtain stream level class data. However, crowd-based observations are uncertain and require data quality control. Therefore, we used a deep learning model to estimate the water-level class for photos taken by citizen scientists at different times for the same stream and compared different options for model training. The models had a root mean square error (R) of 0.5 classes or better for all but four of the 385 sites for which the model was trained. Low water levels were in general predicted better than high water levels (R of 0.6 vs 1.0 classes). The study thus highlights the potential of human-computer interaction for data collection and quality control in citizen science projects.

Keywords

citizen science; deep learning; water-level class; stream; >

Published in

Hydrological Sciences Journal
2023, Volume: 68, number: 13, pages: 1826-1840
Publisher: TAYLOR & FRANCIS LTD

SLU Authors

• Seibert, Jan

  • Department of Aquatic Sciences and Assessment, Swedish University of Agricultural Sciences
    
  • University of Zurich

UKÄ Subject classification

Oceanography, Hydrology, Water Resources


Publication identifier

DOI: https://doi.org/10.1080/02626667.2023.2240312

Permanent link to this page (URI)

https://res.slu.se/id/publ/123787