Type

Journal Article

Authors

Fiona Regan
Alan F. Smeaton
Noel E. O'Connor
Kevin McGuinness
Kevin Murphy
Ciprian Constantin Briciu Burghina
Timothy Sullivan
Dian Zhang

Subjects

Computer Science

Topics
decision support estuary marine robust online clustering anomaly detection turbidity pixel based adaptive segmentation machine learning continuous water monitoring salinity

Detection and classification of anomalous events in water quality datasets within a smart city-smart bay project (2014)

Abstract Continual measurement is key to understanding sudden and gradual changes in chemical and biological quality of water, and for taking reactive remedial action in the case of contamination. Monitoring of water bodies will increase in future within Europe to comply with legislative requirements such as the Water Framework Directive and globally owing to pressure from climate change. Establishing high quality long-term monitoring programs is regarded as essential if the implementation of pertinent legislation is to be successful. However, conventional discrete sampling programs and laboratory-based analysis techniques can be costly, and are unlikely to provide timely and reliable estimates of true ranges of deterministic physicochemical variability in a water body with marked temporal or spatial variability. Only continual or near continual measurements have the capacity to detect ephemeral or sporadic events, thus providing the potential for on-line event detection and classification. The aim of this work is to demonstrate the potential role of continuous data acquisition in decision support as part of a smart city project. In this work, a multi-modal smart sensor network system framework for large scale estuarine and marine water quality monitoring is proposed. The application of a number of evolving techniques that allow automated detection and clustering of events from data generated by in situ sensors within environmental time series datasets is described. We provide examples of how change in the range of variables such as turbidity and salinity might be detected and clustered to provide the end user with greater ability to detect the onset of environmentally significant events. Finally, we discuss the acquisition of data from in situ water quality sensors and its utility within the framework a smart city-smart bay integrated project.
Collections Ireland -> Dublin City University -> Publication Type = Article
Ireland -> Dublin City University -> Subject = Computer Science
Ireland -> Dublin City University -> DCU Faculties and Centres = DCU Faculties and Schools
Ireland -> Dublin City University -> Status = Published
Ireland -> Dublin City University -> DCU Faculties and Centres = Research Initiatives and Centres: CLARITY: The Centre for Sensor Web Technologies
Ireland -> Dublin City University -> DCU Faculties and Centres = DCU Faculties and Schools: Faculty of Engineering and Computing: School of Electronic Engineering
Ireland -> Dublin City University -> DCU Faculties and Centres = Research Initiatives and Centres: Marine and Environmental Sensing Technology Hub (MESTECH)
Ireland -> Dublin City University -> DCU Faculties and Centres = DCU Faculties and Schools: Faculty of Engineering and Computing
Ireland -> Dublin City University -> Subject = Computer Science: Machine learning
Ireland -> Dublin City University -> DCU Faculties and Centres = Research Initiatives and Centres

Full list of authors on original publication

Fiona Regan, Alan F. Smeaton, Noel E. O'Connor, Kevin McGuinness, Kevin Murphy, Ciprian Constantin Briciu Burghina, Timothy Sullivan, Dian Zhang

Experts in our system

1
Fiona Regan
Dublin City University
Total Publications: 101
 
2
Alan F. Smeaton
Dublin City University
Total Publications: 475
 
3
Noel E. O'Connor
Dublin City University
Total Publications: 449
 
4
Kevin McGuinness
Dublin City University
Total Publications: 77
 
5
Kevin Murphy
Dublin City University
Total Publications: 7
 
6
Ciprian Constantin Briciu Burghina
Dublin City University
Total Publications: 12
 
7
Dian Zhang
Dublin City University
Total Publications: 14