Estimating cellular network performance during hurricanes

Booker, Graham; Torres, Jacob M.; Guikema, Seth D.; Sprintson, Alex; Brumbelow, Kelly

doi:10.1016/j.ress.2009.11.003

Cited by 15 publications

(5 citation statements)

References 9 publications

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“…For fixed fiber networks, there is evidence to suggest that these assets can be susceptible to surface and sub-sea physical damage as a result of climate extremes, despite being submerged or buried. In terms of mobile cellular networks, there are a range of different damage states that can affect these assets when subject to climate hazards [73], including damage to (i) onsite backup electricity generation equipment, (ii) active electronic radio equipment, and (iii) other infrastructure assets necessary to provide normal service [74].…”

Section: Telecommunication Infrastructurementioning

confidence: 99%

“…There is currently a large gap in the literature in terms of quantitative risk approaches, as our review only highlighted a very small number of climate risk analyses focused on telecommunication assets. Regional evaluations which consider telecommunication assets include one study assessing the infrastructure impacts from European coastal flooding [75], two quantifying US hurricane impacts [74,76], as well as one global assessment highlighting coastal flooding and tropical storm vulnerability [77]. One key reason for this is a lack of consistent datasets to enable this analysis, as well as a more thorough understanding of how telecom infrastructure could be affected by climate hazards.…”

Section: Telecommunication Infrastructurementioning

confidence: 99%

See 1 more Smart Citation

Quantifying climate risks to infrastructure systems: A comparative review of developments across infrastructure sectors

Verschuur,

Fernández-Pérez,

Mühlhofer

et al. 2024

PLOS Clim

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Infrastructure systems are particularly vulnerable to climate hazards, such as flooding, wildfires, cyclones and temperature fluctuations. Responding to these threats in a proportionate and targeted way requires quantitative analysis of climate risks, which underpins infrastructure resilience and adaptation strategies. The aim of this paper is to review the recent developments in quantitative climate risk analysis for key infrastructure sectors, including water and wastewater, telecommunications, health and education, transport (seaports, airports, road, rail and inland waterways), and energy (generation, transmission and distribution). We identify several overarching research gaps, which include the (i) limited consideration of multi-hazard and multi-infrastructure interactions within a single modelling framework, (ii) scarcity of studies focusing on certain combinations of climate hazards and infrastructure types, (iii) difficulties in scaling-up climate risk analysis across geographies, (iv) increasing challenge of validating models, (v) untapped potential of further knowledge spillovers across sectors, (vi) need to embed equity considerations into modelling frameworks, and (vii) quantifying a wider set of impact metrics. We argue that a cross-sectoral systems approach enables knowledge sharing and a better integration of infrastructure interdependencies between multiple sectors.

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Section: Telecommunication Infrastructurementioning

confidence: 99%

Section: Telecommunication Infrastructurementioning

confidence: 99%

Quantifying climate risks to infrastructure systems: A comparative review of developments across infrastructure sectors

Verschuur,

Fernández-Pérez,

Mühlhofer

et al. 2024

PLOS Clim

View full text Add to dashboard Cite

show abstract

“…Fragility analysis is required to compute the probability of failure of components under certain levels of threat intensity. The concept of fragility curves originates from structural reliability analysis (Booker, Torres, Guikema, Sprintson, & Brumbelow, ; Espinoza, Panteli, Mancarella, & Rudnick, ; Li & Ellingwood, ), and represents the conditional probability of failure of a structural element as a function of disaster strength parameters like wind speed and precipitation, as illustrated in Fig. .…”

Section: Impact Of Natural Hazards On Cismentioning

confidence: 99%

An Optimization‐Based Framework for the Identification of Vulnerabilities in Electric Power Grids Exposed to Natural Hazards

2019

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This article proposes a novel mathematical optimization framework for the identification of the vulnerabilities of electric power infrastructure systems (which is a paramount example of critical infrastructure) due to natural hazards. In this framework, the potential impacts of a specific natural hazard on an infrastructure are first evaluated in terms of failure and recovery probabilities of system components. Then, these are fed into a bi-level attackerdefender interdiction model to determine the critical components whose failures lead to the largest system functionality loss. The proposed framework bridges the gap between the difficulties of accurately predicting the hazard information in classical probability-based analyses and the over conservatism of the pure attacker-defender interdiction models. Mathematically, the proposed model configures a bi-level max-min mixed integer linear programming (MILP) that is challenging to solve. For its solution, the problem is casted into an equivalent one-level MILP that can be solved by efficient global solvers. The approach is applied to a case study concerning the vulnerability identification of the georeferenced RTS24 test system under simulated wind storms. The numerical results demonstrate the effectiveness of the proposed framework for identifying critical locations under multiple hazard events and, thus, for providing a useful tool to help decisionmakers in making more-informed prehazard preparation decisions.

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“…Furthermore, any available information of the attacker's intent of attacking, or on the disruptive event's threat profile to the systems, can be carefully formulated in terms of additional constraints on to narrow down the space ℤ. For instance, the impact of a natural hazard like a hurricane on CI system components is usually quantified, in a probabilistic manner, based on the physical model of the hurricane threat (e.g., gust wind speed) [39] and the fragility models of system components [40]. The resulting failure probabilities of system components can be related to their binary damage state variables through Shannon's information theory.…”

Section: Resilience Assessmentmentioning

confidence: 99%

Game-Theoretic Decision Making for the Resilience of Interdependent Infrastructures Exposed to Disruptions

Fang

Zio

2019

Advanced Sciences and Technologies for Security Applications

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This chapter addresses the challenges associated with assessing and improving the resilience of interdependent critical infrastructure systems under potential disruptive events. A specific set of analytical tools are introduced based on quantitative models of infrastructure systems operation and their functional interdependencies. Specifically, the game-theoretic attacker-defender and defender-attacker-defender modeling techniques are applied to assessing the resilience of interdependent CI systems under worst-case disruptions, and advising policymakers on making pre-disruption decisions for improving the resilience of interdependent infrastructures. A case of interdependent power and gas systems is presented to show the proposed model and highlight the significance of protecting interdependent CIs.

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Estimating cellular network performance during hurricanes

Cited by 15 publications

References 9 publications

Quantifying climate risks to infrastructure systems: A comparative review of developments across infrastructure sectors

Quantifying climate risks to infrastructure systems: A comparative review of developments across infrastructure sectors

An Optimization‐Based Framework for the Identification of Vulnerabilities in Electric Power Grids Exposed to Natural Hazards

Game-Theoretic Decision Making for the Resilience of Interdependent Infrastructures Exposed to Disruptions

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