| Grant number: | 19/23603-9 |
| Support Opportunities: | Regular Research Grants |
| Start date: | May 01, 2020 |
| End date: | April 30, 2024 |
| Field of knowledge: | Engineering - Civil Engineering - Construction Industry |
| Agreement: | 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 |
Abstract
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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