Using Artificial Neural Networks to Assess Earthquake Vulnerability in Urban Blocks of Tehran

Afsari, Rasoul; Shorabeh, Saman Nadizadeh; Lomer, Amir Reza Bakhshi; Homaee, Mehdi; Arsanjani, Jamal Jokar

doi:10.3390/rs15051248

Cited by 15 publications

(9 citation statements)

References 100 publications

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“…The advantage of this system is flexibility in creating a wide range of different scenarios (very optimistic to very pessimistic), whose results can benefit managers and planners with different perspectives. OWA has been used in various modeling in the fields of renewable energy [42,47], urban growth forecasting [54,55], land suitability [56,57], vulnerability and resilience [44,58], crisis management [59,60], etc. To the best of our knowledge, this study is the first to use this model to assess UEQ conditions.…”

Section: Literature Reviewmentioning

confidence: 99%

A Scenario-Based Spatial Multi-Criteria Decision-Making System for Urban Environment Quality Assessment: Case Study of Tehran

Moradi,

Akbari,

Taghavi

et al. 2023

Land

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Spatial evaluation of urban environment quality (UEQ) is a key prerequisite in urban planning and development. The main goal of this study is to present a scenario-based spatial multi-criteria decision-making system for evaluating UEQ. Therefore, stakeholder involvement was conducted and eight environmental criteria and six spatial-functional criteria were identified for five districts of Tehran. The weight of the effective criteria was calculated using the analytic hierarchy process (AHP) model. Then, the ordered weighted averaging (OWA) model was used to prepare UEQ maps in different scenarios, including very pessimistic, pessimistic, intermediate, optimistic, and very optimistic. Finally, the spatial distribution of the district population in different classes of UEQ was evaluated. Among the spatial-functional and environmental criteria, the sub-criteria of population density and air pollution, respectively, had the greatest impact on UEQ. In very pessimistic, intermediate, optimistic, and very optimistic scenarios, approximately 76.7, 51.8, 36.4, 23.7, and 9.8 km2 of the studied area had unsuitable UEQ conditions, respectively. In the very pessimistic scenario, about 37,000 and 1,500,000 people lived in areas with suitable and unsuitable UEQ conditions, respectively. In the very optimistic scenario, the population increased to over 917,000 in areas with suitable UEQ and decreased to 336,000 in those with unsuitable UEQ conditions in terms of both environmental and spatial-functional criteria. The research results showed that a high percentage of the population in the study area live under unsuitable UEQ conditions, which indicates the need for attention to improving the current UEQ conditions. The proposed approach is timely to gain a better understanding of the adverse impact of climate change on human well-being in marginal societies and how climate-resilient urban planning can play a significant role.

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Section: Literature Reviewmentioning

confidence: 99%

A Scenario-Based Spatial Multi-Criteria Decision-Making System for Urban Environment Quality Assessment: Case Study of Tehran

Moradi,

Akbari,

Taghavi

et al. 2023

Land

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“…Building capacity curves for 256 reinforced concrete buildings with between four and seven floors were obtained in [23], where the influence of the structural parameters on the seismic performance was quantified using a set of artificial neural network algorithms. In [24], the assessment of the vulnerability of urban blocks to earthquakes using an artificial neural network-multi-layer perceptron (ANN-MLP) was presented. To train the neural network and compute earthquake vulnerability maps, a combined multi-criteria decision analysis (MCDA) process was adopted.…”

Section: Introductionmentioning

confidence: 99%

Seismic Vulnerability Assessment at an Urban Scale by Means of Machine Learning Techniques

Ferranti,

Greco,

Pluchino

et al. 2024

Buildings

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Seismic vulnerability assessment in urban areas would, in principle, require the detailed modeling of every single building and the implementation of complex numerical calculations. This procedure is clearly difficult to apply at an urban scale where many buildings must be considered; therefore, it is essential to have simplified, but at the same time reliable, approaches to vulnerability assessment. Among the proposed strategies, one of the most interesting concerns is the application of machine learning algorithms, which are able to classify buildings according to their vulnerability on the basis of training procedures applied to existing datasets. In this paper, machine learning algorithms were applied to a dataset which collects and catalogs the structural characteristics of a large number of buildings and reports the damage observed in L’Aquila territory during the intense seismic activity that occurred in 2009. A combination of a trained neural network and a random forest algorithm allows us to identify an opportune “a-posteriori” vulnerability score, deduced from the observed damage, which is compared to an “a-priori” vulnerability one, evaluated taking into account characteristic indexes for building’s typologies. By means of this comparison, an inverse approach to seismic vulnerability assessment, which can be extended to different urban centers, is proposed.

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“…Global statistics indicate that 40% of social and economic damage is caused by natural hazards each year [2]. There are several types of natural hazard that occur throughout the world, including earthquakes, landslides, floods, droughts, fires, tornadoes and severe storms [3]. In the period from 2006 to 2021, natural hazards affected approximately 271 million people per year.…”

Section: Introductionmentioning

confidence: 99%

“…The inverse relationship is expressed by the reciprocal of these numbers. The compatibility index (CI) is calculated in Equation (3).…”

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confidence: 99%

A Spatial Decision Support System for Modeling Urban Resilience to Natural Hazards

Rezaei,

Macioszek,

Derakhshesh

et al. 2023

Sustainability

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A major component of urban management is studying and evaluating urban resilience in order to minimize the effects of natural hazards. This is because of the increasing number of natural hazards occurring worldwide. A spatial decision support system is presented for modeling urban resilience and selecting resilient zones in response to natural hazards. This system is implemented based on 22 criteria, grouped into three categories: demographics, infrastructure, and environmental. The criteria are then standardized using minimum and maximum methods, and their importance is determined by the analytical hierarchy process (AHP). The resilience maps in various scenarios are prepared using the ordered weighted average (OWA) method. Flow accumulation (distance from fault), vulnerable population density (vulnerable population density), and distance from road network (material type) were regarded as the most important criteria for flood resilience (earthquake resilience) from environmental, demographic, and infrastructure criteria, respectively. There are different areas that are considered to have very low resilience depending on the risk attitude. According a pessimistic scenario, 1% of Tehran’s area has very low resilience, while according to an optimistic scenario, 38% has very low resilience. This system can be used by urban planners and policymakers for the purpose of improving resilience to natural hazards in low-resilience areas.

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Using Artificial Neural Networks to Assess Earthquake Vulnerability in Urban Blocks of Tehran

Cited by 15 publications

References 100 publications

A Scenario-Based Spatial Multi-Criteria Decision-Making System for Urban Environment Quality Assessment: Case Study of Tehran

A Scenario-Based Spatial Multi-Criteria Decision-Making System for Urban Environment Quality Assessment: Case Study of Tehran

Seismic Vulnerability Assessment at an Urban Scale by Means of Machine Learning Techniques

A Spatial Decision Support System for Modeling Urban Resilience to Natural Hazards

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