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Related Experiment Video

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Continuous Hydrologic and Water Quality Monitoring of Vernal Ponds
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Reconstructing Missing and Anomalous Data Collected from High-Frequency In-Situ Sensors in Fresh Waters.

Claire Kermorvant1, Benoit Liquet1,2, Guy Litt3

  • 1Laboratoire de Mathématiques et de Leurs Applications de Pau Fédération MIRA, UMR CNRS 5142, Université de Pau et des Pays de l'Adour, 64600 Anglet, France.

International Journal of Environmental Research and Public Health
|December 10, 2021
PubMed
Summary
This summary is machine-generated.

We developed a statistical framework to fill gaps in aquatic sensor data. This method accurately predicts missing nitrate concentrations, improving environmental monitoring and freshwater management.

Keywords:
anomaly correctiongeneralised additive model (GAM)missing data reconstructionremote sensingwater quality

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Area of Science:

  • Environmental Science
  • Data Science
  • Hydrology

Background:

  • In situ sensors provide high-frequency aquatic data but are prone to technical errors, causing data gaps.
  • Missing data in environmental time series can compromise the accuracy of statistical analyses and trend detection.

Purpose of the Study:

  • To present a framework for recovering missing data in aquatic time series using generalized additive and auto-regressive models.
  • To evaluate the framework's accuracy in predicting both single missing observations and contiguous data gaps.

Main Methods:

  • Developed a framework using generalized additive and auto-regressive models to impute missing data.
  • Simulated missing data by randomly removing point data and day/week-long sequences from a two-year nitrate concentration dataset.
  • Utilized water temperature, turbidity, conductance, elevation, and dissolved oxygen as covariates.

Main Results:

  • The framework successfully predicted missing nitrate concentrations, with 72% of single missing values falling within sensor precision.
  • Predictive accuracy decreased with longer data gaps but improved significantly with the inclusion of water quality covariates.
  • Even event-based nitrate peaks were accurately reconstructed when covariates were available.

Conclusions:

  • The proposed framework offers a promising method for accurately predicting missing environmental data.
  • Improved data recovery enhances the reliability of summary statistics and trend analyses for effective freshwater ecosystem management.
  • This approach increases confidence in using high-frequency sensor data for environmental monitoring.