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

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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.