A machine learning-driven spatio-temporal vulnerability appraisal based on socio-economic data for COVID-19 impact prevention in the U.S. counties | ||
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Mohammad Moosazadeh, Pouya Ifaei, Amir Saman Tayerani Charmchi , Somayeh Asadi, and ChangKyoo Yoo*, A machine learning-driven spatio-temporal vulnerability appraisal based on socio-economic data for COVID-19 impact prevention in the U.S. counties, Sustainable Cities and Society(ISSN: 2210-6707, SCI, Top 10% journal –Construction and Building), Elsevier, 83(8), pp.103990 (2022.8) (NRF중견2021R1A2C2007838 & Graduate School specialized in Climate Change) |
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이전글 | Exergy-based weighted optimization and smart decision-making for renewable energy systems considering economics, reliability, risk, and environmental assessments | |
다음글 | Non-Gaussian multivariate statistical monitoring of spatio-temporal wind speed frequencies to improve wind power quality in South Korea |