A machine learning-driven spatio-temporal vulnerability appraisal based on socio-economic data for COVID-19 impact prevention in the U.S. counties-Reference-Cited by-同舟云学术

A machine learning-driven spatio-temporal vulnerability appraisal based on socio-economic data for COVID-19 impact prevention in the U.S. counties

Published:2022-08 Issue: Volume:83 Page:103990
ISSN:2210-6707
Container-title:Sustainable Cities and Society
language:en
Short-container-title:Sustainable Cities and Society

Author:

Moosazadeh Mohammad,Ifaei Pouya,Tayerani Charmchi Amir Saman,Asadi Somayeh,Yoo ChangKyoo^ORCID

Funder

Ministry of Environment

Ministry of Science, ICT and Future Planning

National Research Foundation of Korea

Publisher

Elsevier BV

Subject

Transportation,Renewable Energy, Sustainability and the Environment,Civil and Structural Engineering,Geography, Planning and Development

Reference83 articles.

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3. Development of a vulnerability index for diagnosis with the novel coronavirus, COVID-19, in Washington State, USA;Amram;Health and Place,2020

4. Spatiotemporal analysis of COVID 19 in Turkey;Aral;Sustainable Cities and Society,2022

5. Aurélien Géron. (2019). Hands-on machine learning with Scikit-Learn, Keras and TensorFlow: Concepts, tools, and techniques to build intelligent systems. In O'ReillyMedia. https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/.

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