An innovative approach to correct data from in-situ turbidity sensors for surface water monitoring. (September 2022)

• Record Type: Journal Article
• Authors: Yousif, Meguel; Burdett, Hannah; Wellen, Christopher; Mandal, Sohom; Arabian, Grace; Smith, Derek; Sorichetti, Ryan J.
• Is Part Of: Environmental modelling & software. Volume 155(2022)
• Publication Date: 2022-09
• Subjects: Turbidity -- Sensors -- Data correction -- Surface water monitoring -- Anomalous data -- Multiple imputations; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2022.105461
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: In-situ water quality sensors yield continuous, high-frequency measurements that are essential for long-term monitoring. Optical sensors, such as turbidity sensors, are prone to lens obstruction and ultimately, anomalous measurements. In this study, we developed a novel approach to detect, remove, and replace these anomalies. A free, open-source, software package implementing this approach is provided. Anomaly detection and removal is completely automated, though manual verification is recommended. Sections of missing data are filled by one of two methods, depending on the size of each section. This approach was tested on turbidity measurements from sensors deployed across southern Ontario. Automated detection performance was consistent across all test datasets, with most (>95%) synthetically introduced anomalies removed in all but one dataset. Gap-filling provided accurate estimates for smaller gaps, while performance on larger gaps varied. Overall, this approach as implemented in the provided software can greatly assist with data management in long-term monitoring programs. Highlights: A novel approach to detect, remove, and replace sensor data anomalies was developed. Open-source software implementing this approach is provided. Almost all synthetic anomalies were removed automatically across all test scenarios.