Document / Publication
This guidance note explores how new approaches in machine learning can provide new ways of looking into the complex relationships between models, and the actual understanding of the potential impacts of a hazard on the built environment and society. It aims to provide more accurate, efficient, and useful answers.
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.
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.
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