The social and physical vulnerability assessment of old texture against earthquake (case study: Fahadan district in Yazd City)

Aliabadi, Saeed Fallah; Sarsangi, Alireza; Modiri, Ehsan

doi:10.1007/s12517-015-1939-8

Cited by 24 publications

(9 citation statements)

References 18 publications

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“…It is in line with this that some studies itemize social and physical conditions of communities as two major attributes of disaster vulnerabilities such that their identification and assessment provide better alternatives to reducing risks to disaster. Living attributes of people, social networks, beliefs, customs, and other population characteristics depict social conditions [37], whilst conditions of infrastructures, physical accessibility, and densities highlight the physical aspect [38]. Consequently, approaches to analyzing these conditions have also toed similar paths by categorizing their indicators into social and built environments [39,40].…”

Section: Disasters and Vulnerabilities Of Foreign Residents In Japanmentioning

confidence: 99%

Utilizing Population Distribution Patterns for Disaster Vulnerability Assessment: Case of Foreign Residents in the Tokyo Metropolitan Area of Japan

Adu-Gyamfi

Shaw

2021

IJERPH

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Foreign residents in Japan are amongst the vulnerable groups at risk to disasters in the country. Improvement is crucial in meeting Japan’s vison of zero casualties in major disaster events. If the case of the 2005 Hurricane Katrina is to offer an insight into migrants’ characteristics in mega-disaster situations, then a broader analysis of vulnerabilities is needed to avoid mass casualties should the anticipated megathrust earthquake occur. Hence, this study analyzes the vulnerabilities of foreign residents by utilizing their spatial distribution attributes in the Tokyo metropolitan area. This study uses multiple approaches that combine geographic information systems to analyze secondary and primary datasets. The results reveal that foreign nationals are spatially clustered in some parts of the metropolis, especially within a 7 km radius of Minato city. The densities in these areas alter the earthquake community vulnerability levels from 1.23% to 2.8% and from 5.42% to 13.46%, respectively. Although only 11% of foreign residents are prepared for any disaster, there is a high sense of interaction amongst them and Japanese nationals, which almost eliminates isolation within communities. This study therefore proposes the utilization of some of these attributes in mobilizing specifically targeted evacuation procedures, management of evacuation centers, and disaster risk information dissemination.

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Section: Disasters and Vulnerabilities Of Foreign Residents In Japanmentioning

confidence: 99%

Utilizing Population Distribution Patterns for Disaster Vulnerability Assessment: Case of Foreign Residents in the Tokyo Metropolitan Area of Japan

Adu-Gyamfi

Shaw

2021

IJERPH

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“…In this study, a total of nine conditional factors or parameters with 37 classes from two main indicators were considered. Examples of physical indicators are street pattern, building density, building alignment, open space density, land cover and other built-environment related factors [35,36] and examples of environmental factors include slope, geological features, aspect, fault and other natural-related features [24]. According to [37], in GIS-based studies, the selected factors should be operational, complete, non-uniform, measurable, and non-redundant.…”

Section: Conditional Factorsmentioning

confidence: 99%

Seismic Vulnerability Assessment in Ranau, Sabah, Using Two Different Models

Razak¹,

Rambat²,

Ros³

et al. 2021

IJGI

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Sabah is prone to seismic activities due to its location, being geographically located near the boundaries of three major active tectonic plates; the Eurasian, India-Australia, and Philippine-Pacific plates. The 6.0 Mw earthquake that occurred in Ranau, Sabah, on 15 June 2015 which caused 18 casualties, all of them climbers of Mount Kinabalu, raised many issues, primarily the requirements for seismic vulnerability assessment for this region. This study employed frequency ratio (FR)–index of entropy (IoE) and a combination of (FR-IoE) with an analytical hierarchy process (AHP) to map seismic vulnerability for Ranau, Sabah. The results showed that the success rate and prediction rate for the areas under the relative operating characteristic (ROC) curves were 0.853; 0.856 for the FR-IoE model and 0.863; 0.906 for (FR-IoE) AHP, respectively, with the highest performance achieved using the (FR-IoE) AHP model. The vulnerability maps produced were classified into five classes; very low, low, moderate, high, and very high seismic vulnerability. Seismic activities density ratio analysis performed on the final seismic vulnerability maps showed that high seismic activity density ratios were observed for high vulnerability zones with the values of 9.119 and 8.687 for FR-IoE and (FR-IoE) AHP models, respectively.

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“…To evaluate seismic vulnerability, many studies have applied the analytical hierarchy process (AHP) and multi-criteria decision analysis (MCDA) in conjunction with a geographic information system (GIS). If multiple targets require evaluation, they are stratified and their importance is quantified to determine the relative priorities of their criteria by using factor weighting [3,14,[34][35][36][37].…”

Section: Introductionmentioning

confidence: 99%

Performance of Logistic Regression and Support Vector Machines for Seismic Vulnerability Assessment and Mapping: A Case Study of the 12 September 2016 ML5.8 Gyeongju Earthquake, South Korea

et al. 2019

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In this study, we performed seismic vulnerability assessment and mapping of the ML5.8 Gyeongju Earthquake in Gyeongju, South Korea, as a case study. We applied logistic regression (LR) and four kernel models based on the support vector machine (SVM) learning method to derive suitable models for assessing seismic vulnerabilities; the results of each model were then mapped and evaluated. Dependent variables were quantified using buildings damaged in the 9.12 Gyeongju Earthquake, and independent variables were constructed and used as spatial databases by selecting 15 sub-indicators related to earthquakes. Success and prediction rates were calculated using receiver operating characteristic (ROC) curves. The success rates of the models (LR, SVM models based on linear, polynomial, radial basis function, and sigmoid kernels) were 0.652, 0.649, 0.842, 0.998, and 0.630, respectively, and the prediction rates were 0.714, 0.651, 0.804, 0.919, and 0.629, respectively. Among the five models, RBF-SVM showed the highest performance. Seismic vulnerability maps were created for each of the five models and were graded as safe, low, moderate, high, or very high. Finally, we examined the distribution of building classes among the 23 administrative districts of Gyeongju. The common vulnerable regions among all five maps were Jungbu-dong and Hwangnam-dong, and the common safe region among all five maps was Gangdong-myeon.

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The social and physical vulnerability assessment of old texture against earthquake (case study: Fahadan district in Yazd City)

Cited by 24 publications

References 18 publications

Utilizing Population Distribution Patterns for Disaster Vulnerability Assessment: Case of Foreign Residents in the Tokyo Metropolitan Area of Japan

Utilizing Population Distribution Patterns for Disaster Vulnerability Assessment: Case of Foreign Residents in the Tokyo Metropolitan Area of Japan

Seismic Vulnerability Assessment in Ranau, Sabah, Using Two Different Models

Performance of Logistic Regression and Support Vector Machines for Seismic Vulnerability Assessment and Mapping: A Case Study of the 12 September 2016 ML5.8 Gyeongju Earthquake, South Korea

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