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Weiguo Song

Biographic Data

ID9959310
NAMEWeiguo Song
GIVEN NAMESWeiguo
FAMILY NAMESong
SIGNATURESONG W
AFFILIATIONSUniversity of Science and Technology of China
ORCID0000-0001-5390-6787
VERIFIEDYes
TOTAL WORKS3
TOTAL CITATIONS0
WORKS AS AUTHOR3
WORKS AS EDITOR0
FIRST PUBLICATION YEAR2020
LATEST PUBLICATION YEAR2026
H-INDEX0
  • Machine Learning-Based Wildfire Susceptibility Assessment in California’s WUI

    Open Access•Jianxing Ren, Jun Zhang et al.•ARTICLE•International Journal of Disaster…•2026

    The Wildland-Urban Interface (WUI) in California is increasingly threatened by wildfires due to complex socio-environmental interactions. This study aims to quantify WUI wildfire susceptibility and identify the primary non-linear drivers of fire occurrence. We systematically evaluated multi-source covariates using Wasserstein Distance, LASSO regression, Boruta, and Recursive Feature Elimination to construct highly accurate machine learning models…

  • A comparative study on pedestrian flow through bottlenecks between flood evacuation and land evacuation

    Open Access•Xintong Li, Hang Yu et al.•ARTICLE•International Journal of Disaster…•2023

  • Experimental study on knee and hand crawling evacuation for different age group students

    Open Access•Qiao Wang, Weiguo Song et al.•ARTICLE•International Journal of Disaster…•2020

No prominent works on this page.

  • Experimental study on knee and hand crawling evacuation for different age group students

    Open Access•Qiao Wang, Weiguo Song et al.•ARTICLE•International Journal of Disaster…•2020

  • A comparative study on pedestrian flow through bottlenecks between flood evacuation and land evacuation

    Open Access•Xintong Li, Hang Yu et al.•ARTICLE•International Journal of Disaster…•2023

  • Machine Learning-Based Wildfire Susceptibility Assessment in California’s WUI

    Open Access•Jianxing Ren, Jun Zhang et al.•ARTICLE•International Journal of Disaster…•2026

    The Wildland-Urban Interface (WUI) in California is increasingly threatened by wildfires due to complex socio-environmental interactions. This study aims to quantify WUI wildfire susceptibility and identify the primary non-linear drivers of fire occurrence. We systematically evaluated multi-source covariates using Wasserstein Distance, LASSO regression, Boruta, and Recursive Feature Elimination to construct highly accurate machine learning models…

Engineering (2 works) · Evacuation and Crowd Dynamics (2 works) · Aeronautics (1 work) · Anatomy (1 work) · Bottleneck (1 work) · Chemistry (1 work) · Civil engineering (1 work) · Coastal and Marine Dynamics (1 work) · Computer Science (1 work) · Covariate (1 work)

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