Machine Learning for Disaster Risk Management (English)

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...

DETAILS

Author

Deparday,Vivien;

Gevaert,Caroline Margaux;

Molinario,Giuseppe Maria;

Soden,Robert John;

Balog-Way,Simone Andrea Breunig;
Document Date
2019/01/16
Document Type
Working Paper
Report Number
133787
Volume No
1
Total Volume(s)
1
Country
World,
Region
Other,
Disclosure Date
2019/01/16
Disclosure Status
Disclosed
Doc Name
Machine Learning for Disaster Risk Management
Keywords
disaster risk management

DOWNLOADS

COMPLETE REPORT

Official version of document (may contain signatures, etc)

Official PDF
TXT*
Total Downloads** : 7132
Download Stats
*The text version is uncorrected OCR text and is included solely to benefit users with slow connectivity.

Citation

Deparday,Vivien; Gevaert,Caroline Margaux; Molinario,Giuseppe Maria; Soden,Robert John; Balog-Way,Simone Andrea Breunig.

Machine Learning for Disaster Risk Management (English). Washington, D.C. : World Bank Group. http://documents.worldbank.org/curated/en/503591547666118137

Machine Learning for Disaster Risk Management (English)

DETAILS

DOWNLOADS

COMPLETE REPORT

Citation

SUBSCRIBE TO EMAIL ALERTS