GIS Based Seismic Risk Analysis of Ahmedabad City, India

Thaker, T. P.; Savaliya, Pankaj K.; Patel, Mehul; Patel, Kundan A.

doi:10.1007/978-981-13-0128-5_13

Cited by 2 publications

(2 citation statements)

References 4 publications

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“…For the weights of the four assessment parameters, we used the Analytic Hierarchy Process (AHP) that involves pairwise comparison, weight calculation, and consistency verification, as described by Saaty et al (1990). The AHP process could be used as a rational decision-making tool for risk assessment to stratify, simplify, and systematize multiple criteria (Wen, 2015;Thaker et al, 2018;Providakis et al, 2022). This survey aimed to identify the most important parameters for operators by employing relative comparability.…”

Section: Pairwise Analysis and Assessment Parameter Weightmentioning

confidence: 99%

Assessing network-based earthquake early warning systems in low-seismicity areas

Ahn,

Cho,

Hwang

et al. 2023

Front. Earth Sci.

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Earthquake early warning (EEW) technology, designed to alert the public of earthquake risks after initial P-wave detection but before the onset of strong tremors, has developed rapidly. Methodologies from various fields are combined in EEW systems to estimate earthquake locations, magnitudes, and expected intensities based on the initial P-wave data. These systems operate automatically because prompt responses are required. However, as no common evaluation framework for EEW system verification exists, potentially divergent evaluations from reviewers or countries could ensue. Moreover, evaluating EEW systems is more complicated when the target area does not experience frequent earthquakes. We aimed to establish a guidance review process for low-seismicity areas to ensure reliable and stable integrated EEW system operation. We incorporated management aspects through actual system operator surveillance and designed an EEW assessment process based on feedback from our surveys. Using this approach, we created a comprehensive and well-informed evaluation process that considers the diverse perspectives of experts involved in EEWs. Our proposed assessment method allows for a uniform and consistent evaluation process, regardless of changes in the methods or technologies used by EEW systems. The method aims to guide EEW system assessments in low-seismicity areas.

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Section: Pairwise Analysis and Assessment Parameter Weightmentioning

confidence: 99%

Assessing network-based earthquake early warning systems in low-seismicity areas

Ahn,

Cho,

Hwang

et al. 2023

Front. Earth Sci.

View full text Add to dashboard Cite

show abstract

“…We considered three sub-indicators, among which slope and altitude cause secondary damage by increasing the probability of rocks and structures falling as well as ground failure [13]. Groundwater level is used as an important factor in the impact of a seismic response in the event of a large scale earthquake [51,52]; the groundwater level data were collected according to tubular well locations, and interpolated throughout Gyeongju.…”

Section: Geotechnical Indicatorsmentioning

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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GIS Based Seismic Risk Analysis of Ahmedabad City, India

Cited by 2 publications

References 4 publications

Assessing network-based earthquake early warning systems in low-seismicity areas

Assessing network-based earthquake early warning systems in low-seismicity areas

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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