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多发地区的低收入社区的复原力。重点放在地势低洼的沿海地区，这些地区在东非、巴西和北美的部分地区有很高的干旱和洪水风险。它通过收集有关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，发展居住在贫民窟和河床地区的人们的地理和社会经济知识。项目组还将通过设计和制作使用可持续当地资源材料的负担得起、抗灾能力强的低收入住房系统的原型，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，因此治理将与韧性有关。尽管如此，鉴于城市中心的发展(或缺乏)是现有的社会、经济和政治不平等的结果，政治不平等；不考虑弱势群体独特情况的备灾一揽子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。这项研究还将有助于在时空一级对气候和极端天气事件进行准确的建模，以增加气候科学家的了解，同时使决策者能够作出与灾害有关的决策。机器学习和大数据分析将用于气候建模和确定最优的抗灾住房城市设计和规划政策包，考虑到当前时间至2050年期间预测的与气候变化有关的极端天气情景。虽然大气候数据适合长期气候预测，但本地化和季节性预测的数据仍然不确定和稀疏。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，它既可以处理大数据，也可以处理稀疏数据。

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.