Engineering with Nature: combining Artificial intelligence, Remote sensing and computer Models for the optimum design of coastal protection schemes
Engineering with Nature: combining Artificial intelligence, Remote sensing and computer Models for the optimum design of coastal protection schemes
批准号:
EP/V056042/1
负责人:
Nicoletta Leonardi
金额:
$96.99万
依托单位:
依托单位国家:
英国
项目类别:
Fellowship
财政年份:
2021
资助国家:
英国
项目状态:
未结题
起止时间:
2021 至 --
中文摘要
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英文摘要
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.
期刊论文(10)
专著(0)
科研奖励(0)
会议论文
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DOI:
10.1016/j.scitotenv.2023.161461
发表时间:
2023-01
期刊:
The Science of the total environment
影响因子:
--
作者:
[N. Pannozzo;R. Smedley;A. Plater;I. Carnacina;N. Leonardi]
通讯作者:
N. Pannozzo;R. Smedley;A. Plater;I. Carnacina;N. Leonardi
Coastal forecast through coupling of Deep Learning and hydro-morphodynamical modelling
通过深度学习和水文形态动力学建模的耦合进行海岸预测
DOI:
10.1002/essoar.10512513.1
发表时间:
2022
期刊:
影响因子:
--
作者:
[Kumar P]
通讯作者:
Kumar P
Coastal wetlands and seagrass dynamics with environmental change
沿海湿地和海草随环境变化的动态
DOI:
10.5194/egusphere-egu22-2798
发表时间:
2022
期刊:
影响因子:
--
作者:
[Leonardi N]
通讯作者:
Leonardi N
Editorial: Coastal Wetlands Dynamics
社论:沿海湿地动态
DOI:
10.3389/fmars.2022.857387
发表时间:
2022
期刊:
Frontiers in Marine Science
影响因子:
3.7
作者:
[Leonardi N]
通讯作者:
Leonardi N
DOI:
10.1080/21664250.2023.2233724
发表时间:
2023-07
期刊:
Coastal Engineering Journal
影响因子:
2.4
作者:
[Pavitra Kumar;N. Leonardi]
通讯作者:
Pavitra Kumar;N. Leonardi
共 9 条
海外基金