Reconstructing Missing and Anomalous Data Collected from High-Frequency In-Situ Sensors in Fresh Waters.

ABSTRACT: In situ sensors that collect high-frequency data are used increasingly to monitor aquatic environments. These sensors are prone to technical errors, resulting in unrecorded observations and/or anomalous values that are subsequently removed and create gaps in time series data. We present a framework based on generalized additive and auto-regressive models to recover these missing data. To mimic sporadically missing (i) single observations and (ii) periods of contiguous observations, we randomly removed (i) point data and (ii) day- and week-long sequences of data from a two-year time series of nitrate concentration data collected from Arikaree River, USA, where synoptically collected water temperature, turbidity, conductance, elevation, and dissolved oxygen data were available. In 72% of cas

SUBMITTER: Kermorvant C 

PROVIDER: S-EPMC8657025 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature