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Details of Grant 

EPSRC Reference: EP/V056042/1
Title: Engineering with Nature: combining Artificial intelligence, Remote sensing and computer Models for the optimum design of coastal protection schemes
Principal Investigator: Leonardi, Dr N
Other Investigators:
Researcher Co-Investigators:
Project Partners:
Environment Agency (Grouped)
Department: Geography and Planning
Organisation: University of Liverpool
Scheme: EPSRC Fellowship
Starts: 01 August 2021 Ends: 31 July 2026 Value (£): 761,220
EPSRC Research Topic Classifications:
Artificial Intelligence Coastal & Waterway Engineering
EPSRC Industrial Sector Classifications:
Environment
Related Grants:
Panel History:
Panel DatePanel NameOutcome
14 Jun 2021 Element Fellowship Interview Panel 15, 16 and 17 June 2021 Announced
06 Apr 2021 Engineering Prioritisation Panel Meeting 6 and 7 April 2021 Announced
Summary on Grant Application Form
Currently, 41% of power stations, 17.9% of railway track, 14.3% of railway stations, 33% of wastewater treatment and half a million of properties are at risk of coastal flooding. The average damage to properties is over £260million each year. Hard engineering solutions are becoming economically unviable due to the high costs of construction, maintenance and adaptation to changes in sea level and storms.

For this reason, there is a growing interest in engineering with nature (including the creation of salt marshes, seagrass beds, beach nourishment and mega-nourishment) which offers a more economically viable alternative and also support net Zero-Carbon emissions and local amenities value as highlighted into the 25 years Government plan to improve the environment, FCERM strategies for England, Scotland and Wales. However, despite the growing recognition about the necessity to move towards this greener alternative for coastal protection, there is little to no guidance on the implementation on engineering with nature. There are no quantitative and process-based decision-making tools and guidelines to aid engineers, planners, and governments to select coastal management strategies fit for their unique local environment. There are still many uncertainties in relation to conditions maximizing the establishment and longevity of engineering with nature and uncertainties in relation to their effectiveness.

This fellowship will develop novel understanding necessary to protect coastal infrastructures and coastal communities through widespread adoption of engineering with nature. The fellowship will use a novel combination of remote sensing, artificial intelligence and computer models to provide -for the first-time- design criteria for coastal protection using engineering with nature and knowledge necessary for the choice of the most durable and efficient coastal management type and location. Results will be summarized into an interactive decision support tool which will be distributed to stakeholders and government agencies for a consistent evaluation of pros- and cons of different coastal management interventions including uncertainties in relation to their effectiveness under different sea level rise and storms scenarios.
Key Findings
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Summary
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Further Information:  
Organisation Website: http://www.liv.ac.uk