Date of this Version
8-24-2019
Document Type
Article
Abstract
Since it is not possible to determine the exact time of a natural disaster’s occurrence and the amount of physical and financial damage on humans or the environment resulting from their event, decision-makers need to identify areas with potential vulnerability in order to reduce future losses. In this paper, a GIS-based open source software entitled Seismic-Related Vulnerability Calculation Software (SEVUCAS), based on the Step-wise Weight Assessment Ratio Analysis (SWARA) method and geographic information system, has been developed to assess seismic vulnerability by considering four groups of criteria (i.e., geotechnical, structural, socio-economic, and physical distance to needed facilities and away from dangerous facilities). The software was developed in C# language using ArcGIS Engine functions, which provide enhanced visualization as well as user-friendly and automatic software for the seismic vulnerability assessment of buildings. Weighting of the criteria (indicators) and alternatives (sub-indicators) was done using SWARA. Also, two interpolation methods based on a radial basis function (RBF) and teaching–learning-based optimization (TLBO) were used to optimize the weights of the criteria and the classes of each alternative as well. After weighing the criteria and alternatives, the weighted overlay analysis was used to determine the final vulnerability map in the form of contours and statistical data. The difference between this software and similar ones is that people with a low level of knowledge in the area of earthquake crisis management can use it to determine and estimate the seismic vulnerabilities of their houses. This visualized operational forecasting software provides an applicable tool for both government and people to make quick and correct decisions to determine higher priority structures for seismic retrofitting implementation.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Lee, Saro; Panahi, Mahdi; Pourghasemi, Hamid Reza; Shahabi, Himan; Alizadeh, Mohsen; Shirzadi, Ataollah; Khosravi, Khabat; Melesse, Assefa M.; Yekrangnia, Mohamad; Rezaie, Fatemeh; Moeini, Hamindreza; Pham, Binh Thai; and Ahmad, Baharin Bin, "SEVUCAS: A Novel GIS-Based Machine Learning Software for Seismic Vulnerability Assessment" (2019). Department of Earth and Environment. 42.
https://digitalcommons.fiu.edu/earth_environment_fac/42
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Comments
Originally published in Applied Science.