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Improving data driven decision making through integration of environmental sensing technologies

Sullivan, Timothy orcid logoORCID: 0000-0002-1093-0602, Zhang, Jian, O'Connor, Edel, Briciu Burghina, Ciprian Constantin orcid logoORCID: 0000-0001-8682-9116, Heery, Brendan orcid logoORCID: 0000-0002-8610-5238, Gualano, Leonardo, Smeaton, Alan F. orcid logoORCID: 0000-0003-1028-8389, O'Connor, Noel E. orcid logoORCID: 0000-0002-4033-9135 and Regan, Fiona orcid logoORCID: 0000-0002-8273-9970 (2013) Improving data driven decision making through integration of environmental sensing technologies. In: Ocean 13, 10-13 June 2013, Bergen, Norway.

Abstract
Coastal and estuarine zones contain vital and increasingly exploited resources. Traditional uses in these areas (transport, fishing, tourism) now sit alongside more recent activities (mineral extraction, wind farms). However, protecting the resource base upon which these marine-related economic and social activities depend requires access to reliable and timely data. This requires both acquisition of background (baseline) data and monitoring impacts of resource exploitation on aquatic processes and the environment. Management decisions must be based on analysis of collected data to reduce negative impacts while supporting resource-efficient, environmentally sustainable uses. Multi-modal sensing and data fusion offer attractive possibilities for providing such data in a resource efficient and robust manner. In this paper, we report the results of integrating multiple sensing technologies, including autonomous multi-parameter aquatic sensors with visual sensing systems. By focussing on salinity measurements, water level and freshwater influx into an estuarine system; we demonstrate the potential of modelling and data mining techniques in allowing deployment of fewer sensors, with greater network robustness. Using the estuary of the River Liffey in Dublin, Ireland, as an example, we present the outputs and benefits resulting from fusion of multi-modal sensing technologies to predict and understand freshwater input into estuarine systems and discuss the potential of multi-modal datasets for informed management decisions.
Metadata
Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Refereed:Yes
Uncontrolled Keywords:Environmental monitoring; multi-modal sensing, sensor networks; salinity; estuarine; data fusion; prediction and modelling
Subjects:Biological Sciences > Biosensors
DCU Faculties and Centres:Research Initiatives and Centres > CLARITY: The Centre for Sensor Web Technologies
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
ID Code:18508
Deposited On:20 Jun 2013 11:06 by Fiona Regan . Last Modified 27 Apr 2023 11:29
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