A framework to infill missing data from freshwater high-frequency sensor data
Опубликовано: 22 Июнь 2026
на канале: ACEMS - ARC Centre of Excellence for Mathematical & Statistical Frontiers
на канале: ACEMS - ARC Centre of Excellence for Mathematical & Statistical Frontiers
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ARC Linkage Project Workshop: Revolutionising water quality monitoring in the information age.
Prof Benoit Liquet-Weiland, Macquarie University
Removing anomalous data creates missing values in in-situ sensor data. Benoit Weiland-Liquet demonstrates how time series models that include other water quality variables as covariates can be implemented in a computationally efficient manner, using freely available software, and used to infill missing water-quality data from in-situ sensors deployed in three diverse river systems within the USA.