Understanding the interacting factors that influence social vulnerability: a case study of the 2016 central Italy earthquake

Frigerio, I; Zanini, Francesca; Mattavelli, M; Amicis, M De

doi:10.1111/disa.12403

Cited by 16 publications

(9 citation statements)

References 30 publications

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“…In any case, that data would be very difficult to interpret, as the real (final) figures of economic losses and building usability include the effect of damage accumulation due to the entire seismic sequence, whereas the simulated Amatrice scenario only relates to the first event of August 24, 2016. With regard to the number of deaths, the predictions strongly underestimate the real data (239 deaths in the Amatrice municipality with a total number of 299 casualties in the entire affected area, Frigerio et al 2019). However, this fact is partly justified by the unlucky concurrence of the August 24 earthquake with the very popular Amatrice festival, that used to attract many tourists and holiday-home owners.…”

Section: Comparison Of Consequences On Building Stock and Populationmentioning

confidence: 73%

Comparative analysis of the fragility curves for Italian residential masonry and RC buildings

Porto

Donà

Rosti

et al. 2021

Bull Earthquake Eng

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The Department of Civil Protection (DPC), in compliance with the EU decision 1313/2013 and at the request of the Sendai Framework for Disaster Risk Reduction 2015–2030 to update the disaster risk assessments by various countries, released the latest National Risk Assessment for Italy at the end of 2018. Specifically, as regards the seismic risk assessment, six research units belonging to two centres of competence of the DPC collaborated under its guidance to update the risk maps of the Italian residential heritage. This extensive collaboration complied with the recent Italian code for Civil Protection, which requires a broad scientific consensus for risk assessment. During this research activity, six fragility models were developed, according to some common criteria (four for masonry buildings and two for RC buildings). These models were then implemented by the DPC for the definition of the national seismic risk. Within this context, the aim of this paper is to evaluate the risk results provided by these models, compare their features, and assess and validate their prediction capabilities. In particular, this paper shows the comparison of predicted and observed damage scenarios and consequences on building stock and the population of two seismic events, i.e. L’Aquila 2009 and Amatrice 2016. Furthermore, the paper provides some interesting damage and risk predictions at a national level. Overall, the forecasts and comparisons made in this study demonstrate the validity of the approach adopted by the DPC for the assessment of national seismic risk.

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Section: Comparison Of Consequences On Building Stock and Populationmentioning

confidence: 73%

Comparative analysis of the fragility curves for Italian residential masonry and RC buildings

Porto

Donà

Rosti

et al. 2021

Bull Earthquake Eng

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“…The socioeconomic framework of society plays a vital role in defining flood vulnerability (Usman et al, 2013) under def-inite conditions of social and infrastructure susceptibility in a particular location during a specific period (Fatemi et al, 2017;Frigerio et al, 2019;Ho et al, 2018;Mishra & Sinha, 2020). Thus, the impact of catastrophes differs as the socioeconomic conditions vary from place to place (Usman et al, 2013).…”

Section: Socioeconomic Vulnerabilitymentioning

confidence: 99%

Micro‐spatial flood risk assessment in Nagaon district, Assam (India) using GIS‐based multi‐criteria decision analysis (MCDA) and analytical hierarchy process (AHP)

Bhuyan¹,

Deka

Saikia³

2023

Risk Analysis

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Nagaon is one of the highly flood‐prone districts of Assam, India that recurrently experiences devastating floods resulting in the loss of lives and property and wreaking havoc on the district's socioeconomic infrastructure. Identification and mapping of spatial patterns of flood hazards, flood vulnerability, and flood risk zones (FRZs) of the district are, therefore, crucial for flood management and mitigation. The present study, therefore, attempts to delineate the FRZs of more than 930 villages in the Nagaon district by integrating the flood hazard and vulnerability layers in the geospatial environment using the multi‐criteria decision analysis and analytical hierarchy process techniques. Here, seven flood hazard and vulnerability indicators are considered to derive each layer separately. The results indicate that about 15.14% of the district's total villages are in the very high FRZ, 27.93% in the high, 46.62% in the moderate, and 10.3% in the low FRZ. Further, bivariate correlation analysis is used to evaluate the results with the percentages of the population, cropland, and animals affected by floods at different temporal scales in order to ensure that the revenue circles with a higher percentage of area under high and very high FRZs genuinely have higher percentages of flood‐affected cropland, people, and livestock. The significance of this research is evident in its pragmatic findings that could aid the stakeholders in managing and reducing flood risk at micro‐spatial scales.

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“…In terms of risk management, assuming a physical risk-based approach can be critical. As is widely accepted, in an urban environment, the ex-ante social aggravation or vulnerability levels can be conditioners for future trends or potential paths that may define levels of preparedness and resilience [36][37][38][39]. Sometimes, such social processes may define particular dynamics that may amplify the effects of the hazard [38,40].…”

Section: Comparison Of Seismic Risk Spatial Distribution's Patterns O...mentioning

confidence: 99%

Integral Seismic Risk Assessment through Fuzzy Models

2020

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The usage of indicators as constituent parts of composite indices is an extended practice in many fields of knowledge. Even if rigorous statistical analyses are implemented, many of the methodologies follow simple arithmetic assumptions to aggregate indicators to build an index. One of the consequences of such assumptions can be the concealment of the influence of some of the composite index’s components. We developed a fuzzy method that aggregates indicators using non-linear methods and, in this paper, compare it to a well-known example in the field of risk assessment, called Moncho’s equation, which combines physical and social components and uses a linear aggregation method to estimate a level of seismic risk. By comparing the spatial pattern of the risk level obtained from these two methodologies, we were able to evaluate to what extent a fuzzy approach allows a more realistic representation of how social vulnerability levels might shape the seismic risk panorama in an urban environment. We found that, in some cases, this approach can lead to risk level values that are up to 80% greater than those obtained using a linear aggregation method for the same areas.

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Understanding the interacting factors that influence social vulnerability: a case study of the 2016 central Italy earthquake

Cited by 16 publications

References 30 publications

Comparative analysis of the fragility curves for Italian residential masonry and RC buildings

Comparative analysis of the fragility curves for Italian residential masonry and RC buildings

Micro‐spatial flood risk assessment in Nagaon district, Assam (India) using GIS‐based multi‐criteria decision analysis (MCDA) and analytical hierarchy process (AHP)

Integral Seismic Risk Assessment through Fuzzy Models

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