Details of Award
NERC Reference : NE/S016236/1
Engineering Transformation for the Integration of Sensor Networks: A Feasibility Study - 'ENTRAIN'
Grant Award
- Principal Investigator:
- Professor G Leontidis, University of Lincoln, School of Computer Science
- Co-Investigator:
- Professor S Pearson, University of Lincoln, Lincoln Inst for Agri-Food Technology
- Grant held at:
- University of Lincoln, School of Computer Science
- Science Area:
- Atmospheric
- Freshwater
- Terrestrial
- Overall Classification:
- Unknown
- ENRIs:
- Environmental Risks and Hazards
- Natural Resource Management
- Pollution and Waste
- Science Topics:
- Catchment management
- Dissolved organic carbon
- Ecohydrology
- Flood risk
- Flow pathways
- Forests
- Groundwater
- Hydrologic scaling
- Hydrological cycle
- Hydropedology
- Lakes
- Peatlands
- Rainfall events
- Runoff modelling
- Sediment transport
- Sensor networks
- Snow and ice flows
- Soil moisture
- Uplands
- Water quality
- Water resources
- Water storage
- Hydrological Processes
- Artificial Intelligence
- Decision Support (AI)
- Fuzzy Logic (AI)
- Intelligent Systems (AI)
- Machine Learning (AI)
- Neural Networks (AI)
- Artificial Intelligence
- Survey & Monitoring
- Land - Atmosphere Interactions
- Water Quality
- Abstract:
- There is a need to make use of new digital data analysis techniques to improve our understanding of the environment. Data from a new generation of environmental sensors, combined with analyses based on Artificial Intelligence, has the potential to help us understand from human influences and long-term change are affecting the environment around us. Artificial Intelligence approaches enable computers to identify trends and relationships across different streams of data, often picking out patterns that would be too difficult or time-consuming for humans to identify manually. To realise these benefits, data from diverse sensor networks must combined and analysed together. Currently many sensor networks are operated individually, and data are not readily combined due to differences in the way measurements are made (e.g. between weekly river samples and sub-second measurements of gases in the atmosphere). In addition, to combine these data in an automatic way without human intervention requires much finer and more consistent descriptions of the contents of data streams, so that machines can understand the content sufficiently. Links between sensors in space are also important, and machines will need an understanding of these links, not just in the sense of coordinates, but for example how sensors are linked along rivers. We can construct a digital representation of rivers in order to enable this. We will describe the various elements of a future environmental analysis system that will be required in order to achieve these benefits, and addressing some of these currently missing components. We will look at technologies, from databases to data transfer mechanisms, to understand how a system could be built. We will use data from 3 NERC sensor networks measuring environmental variables from the atmosphere to river water quality, and show how this data can be automatically integrated in such a way that machines would be able to analyse it automatically. A significant issue when monitoring with high-resolution sensors is how to handle problems in the data, which could include missing data, and erroneous values due to sensor failure. There is too much data for humans to manually view and check, and so automated approaches are needed. Currently these are often simple checks of individual data values against expected ranges, but again there are opportunities for artificial intelligence to improve this. AI approaches can look across multiple sensors, identify relationships, and find subtle changes in data signals, and this can be used to both identify data problems and to fix them through infilling. We will enhance the 3 NERC networks by testing and applying such approaches to data quality control. We will investigate some fundamental limitations of high-resolution monitoring, the transfer of large amounts of data from the field site to the data centre, the security of such systems, and whether more processing could be done on the instruments themselves to reduce data transfer volumes. We will meet with the public, with policy-makers, with industry and with researchers to discuss where there will be most to be gained from development of AI approaches to analysing environmental sensor data. We will develop ideas for future work to realise these gains, and will promote the benefits of an integrated system for environmental monitoring. These stakeholders are likely to include the Environment Agency, SEPA, Natural Resources Wales, Defra, Water companies, sensor network developers, and public organisations with an interest in the environment, including the National Trust, the Rivers Trusts, and local community groups.
- Period of Award:
- 4 Feb 2019 - 3 Feb 2020
- Value:
- £92,032 Split Award
Authorised funds only
- NERC Reference:
- NE/S016236/1
- Grant Stage:
- Completed
- Scheme:
- Innovation
- Grant Status:
- Closed
- Programme:
- Digital Environment
This grant award has a total value of £92,032
FDAB - Financial Details (Award breakdown by headings)
| DI - Other Costs | Indirect - Indirect Costs | DA - Investigators | DA - Estate Costs | DI - Staff | DA - Other Directly Allocated | DI - T&S |
|---|---|---|---|---|---|---|
| £2,420 | £38,619 | £9,603 | £6,312 | £31,537 | £1,527 | £2,016 |
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