An integrated framework for assessment of smart city resilience

Khatibi, Hamed; Wilkinson, Suzanne; Eriwata, Graham; Sweya, Lukuba N.; Baghersad, Mostafa; Dianat, Heiman; Ghaedi, Khaled; Javanmardi, Ahad

doi:10.1177/23998083221092422

Cited by 8 publications

(5 citation statements)

References 54 publications

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“…These sources might provide dependable energy alternatives if regular power networks fail during disasters. Utilizing renewable energy more often reduces C.F, lowering reliance on fossil fuels and GHG emissions [46].…”

Section: Renewable Energy Integrationmentioning

confidence: 99%

Enhancing Urban Sustainability: Unravelling Carbon Footprint Reduction in Smart Cities through Modern Supply-Chain Measures

Issa Zadeh,

Garay-Rondero

2023

Smart Cities

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The worldwide Sustainable Development Goals (SDGs) for smart cities and communities focus significant attention on air quality and climate change. Technology and management can reduce fossil fuel dependence in smart cities’ energy supply chains (SC). A sustainable smart city and reduced carbon emissions require coordinated technology and management with appropriate infrastructure. A systematic review of smart city SC management literature that reduces the carbon footprint (C.F) inspired this study. The study shows how each attribute reduces greenhouse gas (GHG) emissions. The Introduction highlights the subject matter and principal goal, which is to investigate how SC management strategies could assist smart cities in lowering their C.F. The Methods and Materials section provides a succinct description of the refining process in Systematic Reviews and Meta-Analyses in Scoping Reviews (PRISMA-ScR) relevant to C.F mitigation in smart city (SC) management. Significant works are described in the Results and Findings section, which exposes how smart cities and SC measurements reduce C.F. The Discussion section examines and scientifically debates the research findings. The Conclusion provides a scientific analysis based on the presented insights and features to enhance how policies must be coordinated to achieve the goal of this research study in a comprehensive way. Furthermore, it provides suggestions for practitioners and governments, and proposals for future research. The main contribution of this paper is conducting and proposing a framework for a better understanding of how the novel digital SCs, their components, and their management practices can help smart cities reduce their C.F.

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Section: Renewable Energy Integrationmentioning

confidence: 99%

Enhancing Urban Sustainability: Unravelling Carbon Footprint Reduction in Smart Cities through Modern Supply-Chain Measures

Issa Zadeh,

Garay-Rondero

2023

Smart Cities

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“…In this context, timely population activity recovery is a key aspect of maintaining functionality of cities. Predictive recovery monitoring is an essential step to ensuring that the functioning of cities return to normal (Khatibi et al, 2022; Yuan et al, 2022a). The existing literature is missing data-driven models and insights to enable predictive urban recovery monitoring (Lee et al, 2022), as the current survey-based approaches are plagued with significant lags {Pinsonneault and Kraemer, 1993 #210}.…”

Section: Introductionmentioning

confidence: 99%

Spatial model for predictive recovery monitoring based on hazard, built environment, and population features and their spillover effects

Patrascu

Mostafavi

2023

Environment and Planning B: Urban Analytics and City Science

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The ability to proactively monitor the trajectory of post-disaster recovery is valuable for resource allocation prioritization. Existing knowledge, however, lacks models and insights for quantifying and proactively monitoring post-disaster community recovery. This study examines models that could predict population activity recovery at the scale of the census block group (CBG). Population activity recovery is measured by using location-based human mobility visitation patterns to essential points-of-interest (POIs) in the context of the 2017 Hurricane Harvey in Harris County, Texas. The study examined the association between the population activity recovery duration and 32 features split into four categories: (1) physical vulnerability and access, (2) hazard exposure and impact, (3) proactive actions and (4) population features. Several types of spatial regression models were evaluated to determine their ability to capture this relationship. The Spatial Durbin Model was identified as the best fit for assessing direct, spillover, and total effects of features on population activity recovery at the CBG level. The results show the extent of physical vulnerability, measured by road network density, prolongs the duration of population activity recovery by a combination of direct and spillover effects. Also, the extent of access to essential facilities, measured based on the number of POIs, shortens the duration of population activity recovery. Correspondingly, the extent of flooding is not a significant feature in explaining the population recovery duration in CBGs. The results show that better preparedness, measured by extent of POIs visitations prior to hurricane landing, is associated with faster population activity recovery. In terms of population attributes, the total number of people, the percentage of minorities, and the percentage of Black and Asian subpopulations are significant features in the model for predicting the duration of population activity recovery. The study outcome offers data-driven insights for understanding the determinants of population activity recovery and provides a new model tool for predictive recovery monitoring based on evaluating the direct, spillover, and total effects of features. These findings can identify areas with slower or more rapid recovery to inform emergency managers and public officials in ensuring equitable resource allocation prioritization.

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“…Providing clean and safe water for its inhabitants and conserving water are crucial aspects of a sustainable and livable urban environment, especially in a smart city [32][33][34].…”

Section: Introductionmentioning

confidence: 99%

Drinking Water Quality Assessment Using a Fuzzy Inference System Method: A Case Study of Rome (Italy)

Gorelova

Bellini

et al. 2023

IJERPH

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Drinking water quality assessment is a major issue today, as it is crucial to supply safe drinking water to ensure the well-being of society. Predicting drinking water quality helps strengthen water management and fight water pollution; technologies and practices for drinking water quality assessment are continuously improving; artificial intelligence methods prove their efficiency in this domain. This research effort seeks a hierarchical fuzzy model for predicting drinking water quality in Rome (Italy). The Mamdani fuzzy inference system is applied with different defuzzification methods. The proposed model includes three fuzzy intermediate models and one fuzzy final model. Each model consists of three input parameters and 27 fuzzy rules. A water quality assessment model is developed with a dataset that considers nine parameters (alkalinity, hardness, pH, Ca, Mg, fluoride, sulphate, nitrates, and iron). These nine parameters of drinking water are anticipated to be within the acceptable limits set to protect human health. Fuzzy-logic-based methods have been demonstrated to be appropriate to address uncertainty and subjectivity in drinking water quality assessment; they are an effective method for managing complicated, uncertain water systems and predicting drinking water quality. The proposed method can provide an effective solution for complex systems; this method can be modified easily to improve performance.

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An integrated framework for assessment of smart city resilience

Cited by 8 publications

References 54 publications

Enhancing Urban Sustainability: Unravelling Carbon Footprint Reduction in Smart Cities through Modern Supply-Chain Measures

Enhancing Urban Sustainability: Unravelling Carbon Footprint Reduction in Smart Cities through Modern Supply-Chain Measures

Spatial model for predictive recovery monitoring based on hazard, built environment, and population features and their spillover effects

Drinking Water Quality Assessment Using a Fuzzy Inference System Method: A Case Study of Rome (Italy)

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