A guidance note on how machine learning can be used for disaster risk management, including key definitions, case studies, and practical considerations for implementation
Evidence-driven disaster risk management (DRM) relies upon many different data types, information sources, and types of models to be effective. Tasks such as weather modelling, earthquake fault line rupture, or the development of dynamic urban exposure measures involve complex science and large amounts of data from a range of sources. Even experts can struggle to develop models that enable the understanding of the potential impacts of a hazard on the built environment and society.
In this context, this guidance note explores how new approaches in machine learning can provide new ways of looking into these complex relationships and provide more accurate, efficient, and useful answers.
The goal of this document is to provide a concise, demystifying reference that readers, from project managers to data scientists, can use to better understand how machine learning can be applied in disaster risk management projects.
Links
Resource collections
- Evaluating humanitarian action
- Innovation
- UN Habitat - Urban Response Collection
- Urban Response - Urban Crisis Preparedness and Risk Reduction
- Urban Response Collection - Community Engagement and Social Cohesion
- Urban Response Collection - Economic Recovery
- Urban Response Collection - Environment and Climate Change
- Urban Response Collection - Housing, Land and Property
- Urban Response Collection - Urban Crisis Response, Recovery and Reconstruction
- Urban Response Collection - Urban Resilience