Coastal Resilience Decision Making with Machine Learning

Our research aims to understand how social data can be integrated with climate data using machine learning for coastal resilience decisions. Although data analytics techniques have been adapted for decision models, incorporating unstructured data is a challenge. We adapt a design science research approach to develop a coastal resilience decision model that can accommodate various sets of climate criteria and social attributes to help us understand coastal risks in communities vulnerable to coastal hazards. We collected social data from environmental groups and individuals and conducted an exploratory social media data analysis on coastal resilience in the greater Boston, U.S., area. We employ non-negative matrix factorization (NMF), a topic modeling technique, to extract human-interpretable topics from a preliminary dataset of 131 documents from 50 different accounts. The outcomes of this research can help community members and policy makers understand and develop robust sustainability and climate focused decisions.