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A low-cost smart sensor network for catchment monitoring

Zhang, Dian orcid logoORCID: 0000-0001-5659-5865, Heery, Brendan orcid logoORCID: 0000-0002-8610-5238, O'Neil, Maria, Little, Suzanne orcid logoORCID: 0000-0003-3281-3471, O'Connor, Noel E. orcid logoORCID: 0000-0002-4033-9135 and Regan, Fiona orcid logoORCID: 0000-0002-8273-9970 (2019) A low-cost smart sensor network for catchment monitoring. Sensors, 19 . pp. 1-22. ISSN 1424-8220

Abstract
Understanding hydrological processes in large, open areas, such as catchments, and further modelling these processes are still open research questions. The system proposed in this work provides an automatic end-to-end pipeline from data collection to information extraction that can potentially assist hydrologists to better understand the hydrological processes using a data-driven approach. In this work, the performance of a low-cost off-the-shelf self contained sensor unit, which was originally designed and used to monitor liquid levels, such as AdBlue, fuel, lubricants etc., in a sealed tank environment, is first examined. This process validates that the sensor does provide accurate water level information for open water level monitoring tasks. Utilising the dataset collected from eight sensor units, an end-to-end pipeline of automating the data collection, data processing and information extraction processes is proposed. Within the pipeline, a data-driven anomaly detection method that automatically extracts rapid changes in measurement trends at a catchment scale. The lag-time of the test site (Dodder catchment Dublin, Ireland) is also analyzed. Subsequently, the water level response in the catchment due to storm events during the 27 month deployment period is illustrated. To support reproducible and collaborative research, the collected dataset and the source code of this work will be publicly available for research purposes.
Metadata
Item Type:Article (Published)
Refereed:Yes
Uncontrolled Keywords:smart sensing; water level monitoring; catchment monitoring; low-cost
Subjects:UNSPECIFIED
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Science and Health > School of Chemical Sciences
Research Initiatives and Centres > INSIGHT Centre for Data Analytics
Publisher:MDPI
Official URL:http://dx.doi.org/10.3390/s19102278
Copyright Information:© 2019 The Authors. Open Access
Funders:Enterprise Ireland (EI) under the Innovation Partnership Feasibility Study in collaboration with Kingspan under grant number IP-2015-0380-Y., Science Foundation Ireland (SFI) under grant number SFI/12/RC/2289.
ID Code:23783
Deposited On:30 Sep 2019 14:30 by Dian Zhang . Last Modified 10 Jan 2023 15:51
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