Optimising Flood Risk Management Interventions in Catchments and Cities

优化流域和城市的洪水风险管理干预措施

• 批准号: 2633795
• 金额: --
• 依托单位: Newcastle University
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2633795
• 关键词: Optimising; Flood; Risk; Management; Interventions

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.

详细的洪水模型用于评估城市和集水区未来增加的洪水风险，并设计洪水衰减功能，包括绿色基础设施（GI）或自然洪水管理（NFM）。重要的是，用于洪水管理的有限资金要明智地使用，最佳地定位GI或NFM功能，以便最大限度地减少流量和雨水管道泄漏。该项目的目的是开发一个自动化框架，用于在未来气候情景下，在集水区和城市中使用一系列NFM或GI进行洪水风险管理方案的优化设计。该框架将提供优化NFM特征的位置、类型、大小和数量的能力，通过机器学习优化算法实现，以最大限度地降低一系列气候情景下给定投资对生命和财产的风险。将对NFM特征的不同“群体”的影响进行多次模拟，由优化算法控制，并迭代直到获得最佳解决方案。这一过程提供了一个新的水平的理解流域动态下不同的洪水情况下，导致一个强大的设计解决方案，供使用的领导洪水管理局。