Flood resilience simulation on DAFNI (Theme 1)

在洪水基础设施发生故障之前、期间和之后采取的行动可能会导致截然不同的结果。糟糕的预警、缓慢的疏散或未能建立临时防洪基础设施都会降低抗灾能力，增加损失并威胁生命。洪水基础设施恢复力模型（FIRM）是一个基于地理空间数据架构的耦合代理和水动力洪水模型，用于探索洪水基础设施故障对洪水恢复力的影响，并测试减轻这些冲击影响的策略。FIRM将地形、建筑和道路网络的遥感信息与经验调查数据相结合，以适应特定社区的特点。对个体的模拟与水动力学模型相结合，以评估他们在洪水事件中的反应和恢复能力。该项目的目的是将FIRM重新编码为Python，以提高互操作性，将FIRM集成到DAFNI中，使其更易于社区访问，通过添加新功能来增强FIRM，以测试更广泛的洪水恢复行动，并提供虚拟和面对面的培训，以支持模型和DAFNI的更广泛采用。

Actions taken before, during and after the shock of flood infrastructure failure can lead to radically different outcomes. Poor warnings, slow evacuation, or failure to erect temporary flood defence infrastructure all reduce resilience, increase damages and threaten lives. The Flood Infrastructure Resilience Model (FIRM) is a coupled agent-based and hydrodynamic flood model built on a geospatial data architecture that is used to explore the impact of the flood infrastructure failure on flood resilience, and to test strategies to mitigate the impact of these shocks. FIRM integrates remotely sensed information on topography, buildings and road networks with empirical survey data to fit characteristics of specific communities. Simulation of individuals has been coupled with a hydrodynamic model to assess their response and their resilience in the event of flooding. The aim of this project is to re-code FIRM into Python for greater inter-operability, integrate FIRM onto DAFNI to make it more accessible to the community, to enhance FIRM by adding new functionality to test a wider set of flood resilience actions, and to provide virtual and in-person training to support the wider uptake of the model and DAFNI.