World Bank / GFDRR · UNDRR · 2019–2023
Machine learning for disaster risk management
How should institutions actually use machine learning for disaster risk — and where does it break? This strand of work translates fast-moving ML methods into practical guidance for the development and humanitarian sectors.
It includes co-authorship of the World Bank / GFDRR guidance note 'Machine Learning for Disaster Risk Management', invited perspectives on how ML is changing flood-risk assessment (NHESS, 2020), and research on earthquake building-damage detection from synthetic-aperture-radar imagery (NHESS, 2023), as well as the GAR2022 global assessment.