Unsupervised Anomaly Detection in Spatio‐Temporal Stream Network Sensor Data

Unsupervised Anomaly Detection in Spatio‐Temporal Stream Network Sensor Data

Abstract The use of in‐situ digital sensors for water quality monitoring is becoming increasingly common worldwide. While these sensors provide near real‐time data for science, the data are prone to technical anomalies that can undermine the trustworthiness of the data and the accuracy of statistica...

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Bibliographic Details
Main Authors: Edgar Santos‐Fernandez, Jay M. Ver Hoef, Erin E. Peterson, James McGree, Cesar A. Villa, Catherine Leigh, Ryan Turner, Cameron Roberts, Kerrie Mengersen
Format: Article
Language:English
Published: Wiley 2024-11-01
Series:Water Resources Research
Subjects:
anomalies
space‐time
linear regression
Bayesian model
sensor data
branching network
Online Access:https://doi.org/10.1029/2023WR035707
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https://doi.org/10.1029/2023WR035707

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