A Bayesian network based approach for modeling and assessing resilience: A case study of a full service deep water port

Hossain, Niamat Ullah Ibne; Nur, Farjana; Hosseini, Seyedmohsen; Jaradat, Raed; Marufuzzaman, Mohammad; Puryear, Stephen M.

doi:10.1016/j.ress.2019.04.037

Cited by 117 publications

(44 citation statements)

References 59 publications

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“…The mutual information method identifies the important variables that should be used in Bayesian models, and thus it improves the accuracy of model results (Yang et al 2018) as it accounts for model uncertainties. The Bayesian method has been used in the literature in different fields including ship emissions (Liu and Duru 2020), shipping accidents (Zhang and Thai 2016), resilience of inland waterways ports (Hosseini and Barker 2016), deep-water port infrastructure resilience (Hossain et al 2019), and classification of port variables (Serrano et al 2018). However, the application of a Bayesian method to forecast port throughput is scant in the scientific literature.…”

Section: Different Port Throughput Forecasting Methodsmentioning

confidence: 99%

“…The Bayesian method has been used in the literature in different fields including ship emissions (Liu and Duru 2020 ), shipping accidents (Zhang and Thai 2016 ), resilience of inland waterways ports (Hosseini and Barker 2016 ), deep-water port infrastructure resilience (Hossain et al. 2019 ), and classification of port variables (Serrano et al. 2018 ).…”

Section: Different Port Throughput Forecasting Methodsmentioning

confidence: 99%

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A model for port throughput forecasting using Bayesian estimation

et al. 2021

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Capacity plays a crucial role in a port's competitive position and the growth of its market share. An investment decision to provide new port capacity should be supported by a growing demand for port services. However, port demand is volatile and uncertain in an increasingly competitive market environment. Also, forecasting models themselves are associated with epistemic uncertainty due to model and parameter uncertainties. This paper applies a Bayesian statistical method to forecast the annual throughput of the multipurpose Port of Isafjordur in Iceland. Model uncertainties are thus taken into account, while parameter uncertainties are handled by selecting influencing macroeconomic variables based on mutual information analysis. The presented model has an adaptive capability as new information becomes available. Our method results in a range of port throughput forecasts, in addition to a point estimate, and it also accounts for epistemic uncertainty, thus increasing the reliability of forecasts. Our results provide support for informed decision-making in capacity planning and management. Our forecasts show a constant linear growth of containerized throughput the period 2020-2025. Noncontainerized throughput declines rapidly over the same period.

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Section: Different Port Throughput Forecasting Methodsmentioning

confidence: 99%

Section: Different Port Throughput Forecasting Methodsmentioning

confidence: 99%

A model for port throughput forecasting using Bayesian estimation

et al. 2021

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“…Therefore, they should be capable of absorbing various component failures [46]. Absorptive capacity includes a set of proactive actions (e.g., by safer design) that should be considered in the CIS's preparedness phase [136][137][138][139]. Robustness is commonly used to quantify the adsorptive CIS capacity [101].…”

Section: What Types Of Terms Define/determine the Resilience Of Technological Ciss?mentioning

confidence: 99%

The Resilience of Critical Infrastructure Systems: A Systematic Literature Review

et al. 2021

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Risk management is a fundamental approach to improving critical infrastructure systems’ safety against disruptive events. This approach focuses on designing robust critical infrastructure systems (CISs) that could resist disruptive events by minimizing the possible events’ probability and consequences using preventive and protective programs. However, recent disasters like COVID-19 have shown that most CISs cannot stand against all potential disruptions. Recently there is a transition from robust design to resilience design of CISs, increasing the focus on preparedness, response, and recovery. Resilient CISs withstand most of the internal and external shocks, and if they fail, they can bounce back to the operational phase as soon as possible using minimum resources. Moreover, in resilient CISs, early warning enables managers to get timely information about the proximity and development of distributions. An understanding of the concept of resilience, its influential factors, and available evaluation and analyzing tools are required to have effective resilience management. Moreover, it is important to highlight the current gaps. Technological resilience is a new concept associated with some ambiguity around its definition, its terms, and its applications. Hence, using the concept of resilience without understanding these variations may lead to ineffective pre- and post-disruption planning. A well-established systematic literature review can provide a deep understanding regarding the concept of resilience, its limitation, and applications. The aim of this paper is to conduct a systematic literature review to study the current research around technological CISs’ resilience. In the review, 192 primary studies published between 2003 and 2020 are reviewed. Based on the results, the concept of resilience has gradually found its place among researchers since 2003, and the number of related studies has grown significantly. It emerges from the review that a CIS can be considered as resilient if it has (i) the ability to imagine what to expect, (ii) the ability to protect and resist a disruption, (iii) the ability to absorb the adverse effects of disruption, (iv) the ability to adapt to new conditions and changes caused by disruption, and (v) the ability to recover the CIS’s normal performance level after a disruption. It was shown that robustness is the most frequent resilience contributing factor among the reviewed primary studies. Resilience analysis approaches can be classified into four main groups: empirical, simulation, index-based, and qualitative approaches. Simulation approaches, as dominant models, mostly study real case studies, while empirical methods, specifically those that are deterministic, are built based on many assumptions that are difficult to justify in many cases.

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“…The United Nations [5] defines resilience as "the ability of a system, community or society exposed to hazards to resist, absorb, accommodate to and recover from the effects of a hazard in a timely and efficient manner, including the preservation and restoration of its essential basic structures and functions." Although expressed by different terminologies, "resist", "recover", "adapt", and "transform" [2,3,6,7] can generally be considered as the main pillars of the resilience concept. There are both theoretical and practical studies in the literature using this modality.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Modeling for Resilience Measurement: NATO Resilience Decision Support Model

Hodicky¹,

Özkan²,

Özdemir³

et al. 2020

Applied Sciences

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Despite its conceptual uncertainty, resilience is mostly about the measurement of capacity. Current studies confirm the importance of resilience measurement and the necessity to support policy makers with a measurement mechanism. A holistic approach considering the measurement of different resilience domains interactively and concurrently is the critical element in this endeavor. In parallel with the rise of popularity of resilience in international organizations, NATO has initiated a project with the objective to discover whether the resilience capacity of a country can be evaluated in a dynamic way via a prototype model execution. The implemented model running both baseline (without any shock) and extraordinary scenarios (with strategic shocks), clearly demonstrates its capacity to represent quantitatively the resilience related factors of a country in the complex operational environment. Moreover, the outputs of the model substantially comply with the resilience concept existing in the literature and NATO applications. One of the main strengths of the model is its almost infinite capacity to create various scenarios and make what-if analysis limited only by the current number of endogenous parameters of the model. It allows studying the secondary and the third order effects of events introduced in scenarios. The user interfaces (input and output dashboards) of the model help decision makers modify the values of selected endogenous parameters, see and compare the time-based values of the resilience factors, and doing so to evaluate risk related to the Area of Operations. Subject matter experts have validated the model and identified the main areas of improvement. The further development brings more countries to the model and implements an aggregation mechanism for output values of both resilience capacity and risk functions. The model will form the core of the NATO Resilience expert system.

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A Bayesian network based approach for modeling and assessing resilience: A case study of a full service deep water port

Cited by 117 publications

References 59 publications

A model for port throughput forecasting using Bayesian estimation

A model for port throughput forecasting using Bayesian estimation

The Resilience of Critical Infrastructure Systems: A Systematic Literature Review

Dynamic Modeling for Resilience Measurement: NATO Resilience Decision Support Model

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