ADRELO: Advancing Resilience in Low Income Housing Using Climate-Change Science and Big Data Analytics

该项目旨在提高生活在灾害易发地区的低收入社区的抗灾能力。重点是东非、巴西和北美某些地区干旱和洪水风险高的低洼沿海地区。它通过收集关于基础设施的地理参考数据和关于项目地点自然遗产的信息，发展贫民窟和河床地区居民的地理和社会经济知识。该项目团队还将通过设计和原型设计一个负担得起的，具有抗灾能力的低收入住房系统，使用可持续的当地资源材料，调查技术采用障碍和扩散驱动因素。城市空间的发展是地理位置、经济历史、城市发展模式的函数，因此治理将对复原力产生影响。尽管如此，考虑到城市中心的发展（或缺乏）是现有社会、经济和政治不平等的结果;不考虑弱势群体独特情况的备灾政策可能会无意中对低收入家庭造成伤害。此外，一揽子政策将包括环境可持续性和公共卫生方面的考虑。该研究还将有助于在时空层面上对气候和极端天气事件进行准确建模，以提高气候科学家的理解，同时增强决策者在灾害相关决策方面的能力。机器学习和大数据分析将用于气候建模，并确定最佳的防灾住房城市设计和规划政策包，考虑到当前和2050年之间与气候变化相关的极端天气情景。虽然大气候数据适用于长期气候预测，但用于局部和季节性预测的数据仍然不确定且稀少。机器学习有潜力处理这种不确定性和数据稀疏性，因为其他应用程序已经证明它可以处理大数据或稀疏数据。

The project aims at enhancing the resilience of low-income communities living in disaster prone areas. The focus is on low-lying coastal zones that have a high risks of droughts and floods in selected parts of East Africa, Brazil and North America. It develops the geographic and socio-economic knowledge of persons living in slum and riverbed areas by gathering georeferenced data on infrastructures and information on the natural heritage of project sites. The project team will also investigate technology adoption barriers and diffusion drivers through designing and prototyping an affordable, disaster-resilient, low-income housing system that use sustainable locally-resourced materials. The development of urban spaces is a function of geographic location, economic history, urban development pattern, and therefore governance will have a bearing on resilience. Still, given that development (or lack thereof) of an urban center is an outcome of existing social, economic, and political inequities political inequities; policy packages for disaster preparedness that do not consider the unique circumstances of vulnerable populations can inadvertently cause harm to low- income households. Furthermore, policy packages will include environmental sustainability and public health considerations. The research will also contribute to accurate modelling of climate and extreme weather events at spatiotemporal level to increase the understanding of climate scientists while empowering policy makers in disaster related decision-making. Machine Learning and Big Data Analytics will be used for climate modelling and to identify optimal disaster resilient-housing urban design and planning policy packages considering projected climate change- related extreme weather scenarios between the current time and 2050. Whilst Big Climate Data is amenable to long-term climate prediction, data for localized and seasonal predictions is still uncertain and sparse. Machine Learning has potential to handle this uncertainty and data sparsity as other applications have demonstrated that it can work with either big data or sparse data.