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| Funder | Natural Environment Research Council |
|---|---|
| Recipient Organization | Newcastle University |
| Country | United Kingdom |
| Start Date | Nov 01, 2021 |
| End Date | Apr 29, 2025 |
| Duration | 1,275 days |
| Number of Grantees | 2 |
| Roles | Student; Supervisor |
| Data Source | UKRI Gateway to Research |
| Grant ID | 2633795 |
Detailed flood models are used to assess increased future flood risk in cities and catchments, and to design flood attenuation features including green infrastructure (GI) or natural flood management (NFM).
It is important that the limited funding available for flood management is spent wisely, locating GI or NFM features optimally so they maximally reduce flows and storm sewer spills.
The aim of this project is to develop an automated framework for the optimal design of flood risk management options using a range of NFM or GI in catchments and cities under future climate scenarios.
The framework will provide the capability to optimise the location, type, size and number of NFM features, achieved by a machine learning optimisation algorithm to minimise risk to life and property for a given investment for a range of climate scenarios.
Multiple simulations of the impact of different "populations" of NFM features will be made, controlled by an optimisation algorithm, and iterated until an optimal solution is obtained.
This process provides a new level of understanding of catchment dynamics under different flood scenarios, resulting in a robust design solution for use by the lead flood authority.
Newcastle University
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