Evaluation of the vulnerability of the worn tissue against the natural hazard of
earthquake using vector machine method (Case example: District 2 of Kerman
city)

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Nohe Sara Maryam; Zakarian Malihe; Al Modarresi Seyed Ali; Khabazi Mostafa; Saraei Mohammad Hossein

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Journal Paper

Paper Information

Journal: JOURNAL OF URBAN SOCIAL GEOGRAPHY Year:2022 | Volume:9 | Issue:1 Page(s): 271-291

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Information Journal Paper

Title

Evaluation of the vulnerability of the worn tissue against the natural hazard of earthquake using vector machine method (Case example: District 2 of Kerman city)

Author(s)

Keywords

VulnerabilityQ4

EarthquakeQ4

Remote SensingQ4

Vector machine techniqueQ4

Kappa coefficientQ4

Abstract

Demonstrate that advanced and civilized people with technology in these settlements show the least sense of danger and can provide the best crisis management in times of crisis.Therefore, considering that Iran is one of the ten most devastating countries and the sixth most Earthquake-prone country in the world, and the dilapidated fabric of Kerman is no exception to this rule, it is necessary to use Remote Sensing techniques such as vector machines to identify and manage Earthquakes.Methods: The present article is applied in terms of purpose and graphical-analytical method. In this study, first, using ASTER satellite images of 2007, worn tissues of Kerman city were identified using the support vector machine classification method. In this study, with a Kappa coefficient of 76% for all classes and a Kappa coefficient of 59%, the worn texture of Kerman was identified.Results: Findings of the research and the final map of the Vulnerability of the two worn-out areas showed that areas with high Vulnerability are 29.87% of the total area of the area, which indicates the inadequacy of the area during the Earthquake. The next ranks of this study include 29.15% moderate Vulnerability, 28.01% very low Vulnerability, 6.74% very high Vulnerability and 6.21% low Vulnerability.The results of this study showed that the support vector machine classification (SVM) method was able to detect nearly 75% of the worn tissue of the area. This identification has shown the high power of the support vector machine method in identifying the area of two urban worn-out structures.

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