Machine Learning for Disaster Risk Management: A guidance note on how machine learning can be used for disaster risk management, including key definitions, case studies, and practical considerations for implementation

Published 2018
Type Guidelines
Theme Disaster Risk Reduction
Language English
Pages: 51
Uploader Marije van der Wal
Publisher(s) GFDRR, The World Bank
Sector(s)
Location(s) Worldwide, Guatemala City, St. Lucia, Sri Lanka, San Francisco
Author(s) Vivien Deparday, Caroline Gevaert, Giuseppe Molinario, Robert Soden, Simone Balog-Way
Rights © 2018 International Bank for Reconstruction and Development/International Development Association or The World Bank
Source: Machine Learning for Disaster Risk Management
Find more on these topics machine learning, ML, digitalization, humanitarian response, disaster risk management, DRM, disaster risk reduction, DRR, guidelines, data organisation, artificial intelligence, AL, earthquake vulnerability, hurricane vulnerability, flood vulnerability, poverty, hazard mapping, damage prediction, wildfire prediction, hazard prediction, assessment
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This document can also be found on the official website of The Global Facility for Disaster Reduction and Recovery (GFDRR) at: https://www.gfdrr.org/ 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. This resource is part of the collection "Digitalizing humanitarian action". Collections are monitored daily and new content is added.