Operationalising resilience: A methodological framework for assessing urban resilience through System Dynamics Model

Datola, Giulia; Bottero, Marta Carla; Angelis, Elena De; Romagnoli, Francesco

doi:10.1016/j.ecolmodel.2021.109851

Cited by 64 publications

(27 citation statements)

References 94 publications

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“…Furthermore, a multi-hazard modeling approach can be implemented within the mathematical framework, consisting of risk analysis, analytic hierarchy processing, and fuzzy logic to examine multi-hazard interactions and to provide a comprehensive risk assessment of the water resources systems ( Jiang & Zhang 2008;Greco et al 2012;Hwang et al 2015;Assumma et al 2019;Nozari et al 2021;Datola et al 2022;De Angeli et al 2022). A general framework for multi-hazard modeling is based on the use of component operating states database of the water resources system, where the failure scenarios are simulated via the application of the deterministic Monte-Carlo (MC) approach to determine a wide range of water system behavior for each scenario (King & Simonovic 2020).…”

Section: Graphical Abstract Introductionmentioning

confidence: 99%

Assessment of water resources system resilience under hazardous events using system dynamic approach and artificial neural networks

Stojković¹,

Marjanović²,

Rakić

et al. 2023

Journal of Hydroinformatics

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The objective of this research is to propose a novel framework for assessing the consequences of hazardous events on a water resources system using dynamic resilience. Two types of hazardous events were considered: a severe flood event and an earthquake. Given that one or both hazards have occurred and considering the intensity of those events, the main characteristics of flood dynamic resilience were evaluated. The framework utilizes an artificial neural network (ANN) to estimate dynamic resilience. The ANN was trained using a large, generated dataset that included a wide range of situations, from relatively mild hazards to severe ones. A case study was performed on the Pirot water system (Serbia). Dynamic resilience was derived from the developed system dynamics model alongside the hazardous models implemented. The most extreme hazard combination results in the robustness of 0.04, indicating a combination of an earthquake with a significant magnitude and a flood hydrograph with a low frequency of occurrence. In the case of moderate hazards, the system robustness has a median value of 0.2 and the rapidity median value of 162 h. The ANN's efficacy was quantified using the average relative error metric which equals 2.14% and 1.77% for robustness and rapidity, respectively.

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Section: Graphical Abstract Introductionmentioning

confidence: 99%

Assessment of water resources system resilience under hazardous events using system dynamic approach and artificial neural networks

Stojković¹,

Marjanović²,

Rakić

et al. 2023

Journal of Hydroinformatics

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“…There is a vast body of literature on community resilience and its determinants. Existing research has mainly focused on resilience to threats such as natural disasters, climate change impacts, and socio-economic difficulties [ [20] , [21] , [22] , [23] , [24] ]. However, resilience to pandemics, specifically before the COVID-19 outbreaks, has been relatively understudied [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

Community resilience to pandemics: An assessment framework developed based on the review of COVID-19 literature

Suleimany

Mokhtarzadeh²,

Sharifi

2022

International Journal of Disaster Risk Reduction

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“…Previous research studies on the evaluation of socio-ecological resilience mainly focus on the urban system (Bush and Doyon, 2019;Cariolet et al, 2019;Datola et al, 2022;Potapova et al, 2022;Wang et al, 2022), agricultural system (Gil et al, 2017), or food system (Tendall et al, 2015;Jacobi et al, 2018). Few studied CSRCC, which mainly centralized on the qualitative analysis of the definitions (Meyer, 2020) or connotations (Tendall et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Cropland System Resilience to Climate Change at Municipal Scale Through Robustness, Adaptability, and Transformability: A Case Study of Hubei Province, China

et al. 2022

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A cropland system is one of the most sensitive socio-ecological systems to climate change, such as drought and flood. Facing frequent extreme weather events worldwide, how to improve cropland system resilience to climate change (CSRCC) and thus ensure food production has been concerned. Although a small number of studies have attempted to evaluate CSRCC through single or multiple indicators, few studies have considered the perspective of the three basic capacities of resilience (i.e., robustness, adaptability, and transformability), which could ignore the dynamic characteristics of cropland system resilience against shocks within a certain period. Therefore, this study first constructs an evaluation index system from the three capacities of system resilience. Then, taking Hubei province, China, as a case and comprehensively using the methods of Delphi, AHP, and TOPSIS to assess the spatio-temporal characteristics of CSRCC at the municipal scale from 2011 to 2018. On this basis, the regional disparities of CSRCC are analyzed by using the Theil coefficient. The results show that the CSRCC of Hubei province fluctuates on a downward trend, with the lowest in 2017 and the highest in 2013. Most municipalities have witnessed a pattern of fluctuated decline, except for a few ones in the plains, such as Wuhan and Jingmen. Generally, municipalities in the plains have greater scores, while some municipalities in the southern and eastern hilly regions show higher adaptability and transformability. In addition, adaptability contributes the least to the CSRCC at the municipal scale. At last, indicator selection against different research objects, influencing mechanism of CSRCC, and policy implications are discussed. This study is expected to provide a reference for the practice in sustainable management and utilization of cropland systems.

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Operationalising resilience: A methodological framework for assessing urban resilience through System Dynamics Model

Cited by 64 publications

References 94 publications

Assessment of water resources system resilience under hazardous events using system dynamic approach and artificial neural networks

Assessment of water resources system resilience under hazardous events using system dynamic approach and artificial neural networks

Community resilience to pandemics: An assessment framework developed based on the review of COVID-19 literature

Evaluation of Cropland System Resilience to Climate Change at Municipal Scale Through Robustness, Adaptability, and Transformability: A Case Study of Hubei Province, China

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