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Advancing resilience in low income housing using climate-change science and big data analytics

Grant number: 19/23603-9
Support Opportunities:Regular Research Grants
Duration: May 01, 2020 - April 30, 2024
Field of knowledge:Engineering - Civil Engineering - Construction Industry
Convênio/Acordo: Belmont Forum
Principal Investigator:Sérgio Francisco dos Santos
Grantee:Sérgio Francisco dos Santos
Principal researcher abroad: Esther Adhiambo Obonyo
Institution abroad: Pennsylvania State University, United States
Principal researcher abroad: George Onyango Okeyo
Institution abroad: De Montfort University, England
Host Institution: Faculdade de Engenharia (FEG). Universidade Estadual Paulista (UNESP). Campus de Guaratinguetá. Guaratinguetá , SP, Brazil
Associated researchers:Holmer Savastano Junior


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 Brazil, East Africa 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 natural heritage of potential 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 governance will have a bearing on resilience. The development (or lack thereof) of an urban center is an outcome of existing social, economic, and 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. Environmental sustainability and public health considerations will be included. Machine Learning and Big Data Analytics will be used 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 data is amenable to long-term climate prediction, data for localized and seasonal predictions is still uncertain and sparse. Machine Learning has potential. Other applications have demonstrated that it can work with either big data or sparse data. The research will contribute to accurately modelling climate and extreme weather events at spatio-temporal level to increases the understanding of climate scientists while empowering policy makers in disaster related decision-making. (AU)

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