SEVUCAS: A Novel GIS-Based Machine Learning Software for Seismic Vulnerability Assessment Article

Lee, Saro, Panahi, Mahdi, Pourghasemi, Hamid Reza et al. (2019). SEVUCAS: A Novel GIS-Based Machine Learning Software for Seismic Vulnerability Assessment . APPLIED SCIENCES-BASEL, 9(17), 10.3390/app9173495

Open Access International Collaboration

cited authors

  • Lee, Saro; Panahi, Mahdi; Pourghasemi, Hamid Reza; Shahabi, Himan; Alizadeh, Mohsen; Shirzadi, Ataollah; Khosravi, Khabat; Melesse, Assefa M; Yekrangnia, Mohamad; Rezaie, Fatemeh; Moeini, Hamidreza; Binh, Thai Pham; Bin Ahmad, Baharin

sustainable development goals

authors

publication date

  • September 1, 2019

published in

keywords

  • ARTIFICIAL-INTELLIGENCE APPROACH
  • BIOGEOGRAPHY-BASED OPTIMIZATION
  • Chemistry
  • Chemistry, Multidisciplinary
  • EARTHQUAKE HAZARD
  • Engineering
  • Engineering, Multidisciplinary
  • FUZZY INFERENCE SYSTEM
  • GENETIC ALGORITHM
  • GIS
  • LOGISTIC-REGRESSION
  • Materials Science
  • Materials Science, Multidisciplinary
  • NAIVE BAYES TREE
  • Physical Sciences
  • Physics
  • Physics, Applied
  • RBF
  • SEVUCAS
  • SOCIAL VULNERABILITY
  • SPATIAL MULTICRITERIA ANALYSIS
  • SUSCEPTIBILITY ASSESSMENT
  • SWARA
  • Science & Technology
  • TLBO
  • Technology
  • Tehran
  • seismic retrofitting
  • seismic vulnerability assessment

Digital Object Identifier (DOI)

publisher

  • MDPI

volume

  • 9

issue

  • 17