A big data-driven dynamic estimation model of relief supplies demand in urban flood disaster-Reference-Cited by-同舟云学术

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A big data-driven dynamic estimation model of relief supplies demand in urban flood disaster

Published:2020-10 Issue: Volume:49 Page:101682
ISSN:2212-4209
Container-title:International Journal of Disaster Risk Reduction
language:en
Short-container-title:International Journal of Disaster Risk Reduction

Author:

Lin Anqi,Wu Hao,Liang Guanghua,Cardenas-Tristan Abraham,Wu Xia,Zhao Chong,Li Dan

Funder

National Key R&D Program of China

Hubei province technical innovation special major project

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

Geology,Safety Research,Geotechnical Engineering and Engineering Geology

Reference53 articles.

1. Recent and future climate change in northwest China;Shi;Climatic Change,2007

2. Spatial prediction of flood susceptibility using random-forest and boosted-tree models in Seoul metropolitan city, Korea;Lee;Geomatics, Nat. Hazards Risk,2017

3. Spatial–Temporal snapshots of global natural disaster impacts Revealed from EM-DAT for 1900-2015;Shen;Geomatics, Nat. Hazards Risk,2019

4. Assessment of pluvial flood exposure and vulnerability of residential areas;Grahn;Int. J. Disaster Risk Reduct.,2017

5. Social vulnerability to floods: review of case studies and implications for measurement;Rufat;Int. J. Disaster Risk Reduct.,2015

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