Resilience Recovery Research Articles

Multi-agent deep reinforcement learning based decision support model for resilient community post-hazard recovery

After a city-scale natural hazard, policymakers should plan sound decisions on the repair sequence to ensure the resilient recovery of the community, which consists of interdependent infrastructures. Stochastic scheduling for repairing interdependent infrastructure systems is a difficult control problem with huge decision spaces. This study proposes a novel decision support model to determine the optimal restoration policies for the purpose of maximizing disaster resilience. A simulation environment is first developed, consisting of hazard intensity assessment, components damage evaluation, system recovery simulation, and resilience quantification. The graph theory is utilized to represent the interdependencies among different systems, and the heterogeneous graph neural network is integrated into this framework to extract the topology and interdependency information of the whole community. The optimal repair policies approximated by neural networks are trained by a multi-agent deep reinforcement learning algorithm, considering uncertainties of the restoration process. The superiority and efficiency of the proposed method are demonstrated through a case study of the Tsinghua University campus, where different decision-making objectives are considered. The results show that the recovery trajectories determined by the proposed model have the highest performance compared to conventional methods. Besides, the proposed methodology based on transfer learning can achieve high computational efficiency for new damage scenarios. This model is promising to be a high-performance, robust decision-support tool for post-hazard repairing decisions.

Post Disaster Damage Assessment Using Ultra-High-Resolution Aerial Imagery with Semi-Supervised Transformers.

Preliminary damage assessments (PDA) conducted in the aftermath of a disaster are a key first step in ensuring a resilient recovery. Conventional door-to-door inspection practices are time-consuming and may delay governmental resource allocation. A number of research efforts have proposed frameworks to automate PDA, typically relying on data sources from satellites, unmanned aerial vehicles, or ground vehicles, together with data processing using deep convolutional neural networks. However, before such frameworks can be adopted in practice, the accuracy and fidelity of predictions of damage level at the scale of an entire building must be comparable to human assessments. Towards this goal, we propose a PDA framework leveraging novel ultra-high-resolution aerial (UHRA) images combined with state-of-the-art transformer models to make multi-class damage predictions of entire buildings. We demonstrate that semi-supervised transformer models trained with vast amounts of unlabeled data are able to surpass the accuracy and generalization capabilities of state-of-the-art PDA frameworks. In our series of experiments, we aim to assess the impact of incorporating unlabeled data, as well as the use of different data sources and model architectures. By integrating UHRA images and semi-supervised transformer models, our results suggest that the framework can overcome the significant limitations of satellite imagery and traditional CNN models, leading to more accurate and efficient damage assessments.

MAPPING FIRE SEVERITY FROM RECENT CALIFORNIA WILDFIRES USING SATELLITE IMAGERY

Abstract. Urban sprawl has become a huge concern for cities like Los Angeles, New York, and Chicago in recent years. As urban sprawl pushes urbanization into city suburbs and outskirts, forest fragmentation becomes evidently prevalent and exposes forests to high temperatures, pollution, pests, and fires that threaten forest health. A 2021 report titled Rebuilding for a Resilient Recovery affirmed that the frequency and damage potential of wildfires have been exacerbated by climate change and urban sprawl especially in California. Globally, these fires can be attributed to both natural and anthropogenic drivers such as deforestation, agriculture, mining, and industrialization. Future projections predict that these incidences of fires will only worsen as the planet continues to warm further, with emphasis on the spread and intensities of the annual California wildfires over the decade. Quantifying the consequences of these fires on global climate change has become crucial and with the emergence of advanced GIS mapping tools, focus, visualization, and interpretation of fire and burn severity has become easier. However, knowledge and understanding of wildfire dynamics is limited especially in terms of fuel load, impacts on vegetation health, aerosol release and associated movement in the atmosphere. It is therefore important to address these gaps to make better and informed actions towards forest use, protection, management, and policies and broadly towards ambitious climate goals such as the UN’s Carbon Neutral goal by 2050. This study uses Sentinel 2A data from the Copernicus fleet between 2018 and 2022 to identify and assess the burn severity of affected areas in Sonoma County, California. The aim of the study is to understand the impacts of fires of fire on vegetation health and the post-fire recovery process. The Normalized Burn Ration Index (NBRI) was used to identify and measure the extent of the burnt areas within the county and their severity and Normalized Difference Vegetation Index (NDVI) was used as a measure of forest heath. The results show that Sonoma County has become a high burn severity area with a major decrease in unburned areas between 2018 and 2022. NDVI values recorded all decrease from January to December for all the years because of pre-fire season drought. The wildfire season begins in May and before then there are seasonal droughts that occur hence accounting for the initial decline in NDVI. The least values recorded were between 0.5 and 0.57 for September, indicating sparse and unhealthy vegetation because of sharp declines during the fire season.

Strengthening resilience in reconstruction after extreme events – Insights from flood affected communities in Germany

Disaster resilience and building back better (BBB) are key concepts in the disaster risk and resilience discourse; however, these concepts often remain vague for many stakeholders involved in recovery. Based on the reconstruction process in Germany after the extreme floods of 2021 that caused more than 180 deaths, we explore challenges and opportunities to strengthen resilient recovery in one of the world's wealthiest and most technologically advanced countries. We examine factors that contributed to severe losses and damages and assess different phases of the reconstruction process. In addition, we identify and discuss measures to support resilience building, focusing particularly on issues of land management, planning and infrastructure. Our findings provide new insights into how funding schemes and planning approaches contribute to or block resilience building and BBB. The results are also highly relevant for other world regions hit by extreme events and for the international discourse on disaster resilience, loss and damage and BBB, for example, how funding arrangements and quality criteria need to be designed to support disaster resilient reconstruction.

Low Carbon Consumer Lending Fintech Product Design Report

In short, green growth will play a central role on the road to a more resilient recovery, and financial technology will be a key driver. Fintech, as an essential support in building a green financial system, will play a key role in supporting green finance to serve the real economy more efficiently.This article focuses on the design of low-carbon consumer credit as a financial technology product, given the high costs of green transformation for businesses and the financing and credit risks they face. Through big data, cloud computing, machine learning and other technologies, the product will help businesses to go green, create value for users and bring positive energy to society. In response to the national “dual carbon” target, low-carbon consumer credit will become an important driver to foster the development of emerging industries. This design differs from generic financial technology products in that the design focuses on green, economic benefits, while taking into account corporate social responsibility, and the use of machine learning to create an anti-fraud system throughout credit risk management, loan withdrawal, and detection of all aspects to ensure maximum security for users.

Global or Local Spatial Spillovers? Industrial Diversity and Economic Resilience in the Middle Reaches of the Yangtze River Urban Agglomeration, China

A growing body of literature has studied the empirical relationship between industrial diversity and economic resilience since the 2008 Great Recession. However, many existing studies are based on a nonspatial perspective, and little is known about the local or global spatial spillover effect of industrial diversity on economic resilience. This paper employs Bayesian spatial econometric methods to investigate the roles of related variety and unrelated variety on economic resilience in the middle reaches of the Yangtze River urban agglomeration, China and explores the possible local or global spatial spillover effect in the diversity–resilience relationship. The empirical results from the spatial Durbin error model estimation show that: (1) regions with high levels of related variety are economically resilient to the external shock in the postcrisis era, whereas unrelated variety has no significant direct effect on recovery resilience; (2) both related and unrelated variety have local spatial spillovers with respect to the one-year resilience of 2008–2009, but these spillovers are negligible in longer study periods. These results confirm the role of industrial relatedness and immediate neighbors in promoting regions’ short-run capabilities of recovery from external economic shocks.

How Has Online Digital Technology Influenced the On-Site Visitation Behavior of Tourists during the COVID-19 Pandemic? A Case Study of Online Digital Art Exhibitions in China

The COVID-19 pandemic has had a significant impact on the global tourism industry, leading to a decrease in peoples’ willingness to travel and a sense of insecurity regarding tourist destinations. Therefore, restoring people’s willingness to travel is the greatest challenge faced by this industry in the post-pandemic era. The tourism industry requires innovative solutions to achieve sustainable recovery. While there is a considerable amount of research on its recovery during the pandemic, there are few studies exploring people’s willingness to travel to encourage sustainable and resilient recovery in the post-pandemic era. This study employed a quality model to examine the satisfaction and intention of tourists towards the application of online digital art exhibitions under the influence of COVID-19. The aim was to investigate the promoting role of online digital art exhibitions in the sustainability and resilient recovery of the tourism industry. To achieve these objectives, this study focuses on the online digital art exhibition of Song Dynasty figure paintings launched by China Central Television (CCTV), with post-exhibition surveys conducted and 512 valid questionnaires collected. The research model and hypotheses are tested using structural equation modeling. The results of this study indicate that travelers’ intentions to engage in on-site visits through online digital exhibitions are determined by three factors: perceived value, satisfaction, and art therapy. Furthermore, online digital art exhibitions not only represented the most important form of tourism during the pandemic, but they also provided significant psychological healing. They have become a driving force for the transformation of the current culture and tourism industry and the promotion of its sustainable development. This research provides a benchmark for future research on the tourism industry, and it offers new research directions in the field of sustainable tourism.

Resilience-oriented recovery of flooded road networks during mega-sport events: a novel framework

This research presents a novel framework that employs the resilience index and metrics, building on previous studies, to conduct a sequential evaluation of flooded road network performance at each timestep throughout the recovery stage. Subsequently, the mathematical integration of the performance-time diagram produces the Recovery Resilience Index (RRI) used to evaluate any recovery strategy/sequence. Furthermore, a sensitivity analysis ensures the consistency of the results. As a case study, this research evaluated three post-flood interventions: Flood-susceptibility-based; Centrality-based; and Critical-links-first recovery strategies, within the context of the FIFA World Cup in Qatar, 2018. The results showed that the best strategy to be dealing with the critical-links-first and flood-susceptibility strategy to be the worst. The suggested framework will expand the options for recovery planning and resource allocation. Also, it will provide the policymakers with a recommendation to improve the recovery process during FIFA World Cup in Qatar, 2018 and any future mega event.

Social transformation in the cruise industry during the COVID-19 pandemic

Social transformation is an emerging trend and a new phenomenon in the cruise industry in the 21st century. Cruise lines encounter stiff competition with many competitors and face sophisticated and unpredictable challenges from the wave of social transformation. Furthermore, the outbreak of the COVID-19 pandemic has accelerated the social transformation phenomena in the industry. This study investigates how social transformation reshapes the cruise industry to discuss the remarkable social and economic changes in the industry. The study builds upon the 4C descriptive framework to suggest how cruise lines take measures to create resilience against the influence affected by social transformation. The study is conducted through 18 semi-structured and in-depth interviews with cruise terminals, travel agencies, logistics, and tourism associations, researchers, cruise lines and passengers, and airlines. The cruise shipping industry structure has fundamentally shifted from supply-driven to demand-driven. The concept of social transformation becomes vital and is a driving force that is more society specific. Findings are drawn as valuable guidelines for cruise lines to scale up in operations and strategies that create social transformation. Cruise lines can also maintain sustainable development and resilient recovery post-COVID-19

Urban resilience, digital technologies, and the economic recovery of a city from the pandemic

AbstractThe COVID‐19 pandemic has devastated the world economy, which has made global economic recovery a considerable challenge. When a city experiences disasters, it needs to utilize innovative governance instruments, such as digital technologies, to foster economic recovery resilience. This article proposes a theoretical framework aimed at revealing the interconnection between digital technologies and economic recovery resilience, and it presents the case of Hangzhou, China, to illustrate this framework. This article finds that digital technologies can foster economic recovery resilience by sustaining a collaborative network during disasters. This article also reveals that government‐sponsored platforms, public‐private partnerships, and citizen coproduction are the underlying dynamics through which digital technologies build resilience. This article provides a nuanced understanding of how digital technologies might impact the theory and practice of economic recovery resilience in the digital era.

The Application of POAC Management in the Tourism Sector in Post-Pandemic Economic Recovery Based on E-Commerce

This study analyzes the management carried out to carry out economic recovery in the tourism sector affected by the Covid-19 pandemic. From time to time, this pandemic continues to find solutions. So that the national and global economy can recover as before. Some countries have carried out various stages of economic recovery, and the fact is that the covid-19 pandemic is still affecting world events. The covid-19 virus first appeared in Wuhan, Hubei, China, in December 2019. As of October 2021, more than 238,662,395 cases have been reported worldwide. Of course, this has a huge impact on the world economy, especially harming the tourism industry. Because the pandemic has imposed social restrictions on the country, both inter-regional and international tourists. This study discusses the implementation of POAC management including 1.) Planning work to achieve economic recovery and national resilience; 2) organizational effects of economic recovery and national resilience; 3) drivers of economic and national resilience recovery; 4) regulations impacting economic recovery and defense. Based on a review of a previous journal published.

Building back better: Granular energy technologies in green recovery funding programs

Building back better: Granular energy technologies in green recovery funding programs

Resilience assessment and recovery of distribution network considering the influence of communication network

Aiming at the existing research ignoring the influence of communication network (CN) on resilience recovery strategy and evaluation of distribution network (DN), this paper will discuss the impact of communication network state on resilience recovery and construct corresponding quantitative evaluation indexes. Firstly, a comprehensive importance evaluation index is constructed for communication failures to determine the order of communication restoration in the failure area. Then the emergency communication recovery in the fault area is carried out by using 5G micro base station. Second, a quantitative evaluation index of the impact of communication on distribution network status recovery is established, considering the fluctuation of distributed power output and load. At the same time, thermal rating constraints are considered in load transfer and island formation, and the effects of static and dynamic thermal rating (DTR) on load recovery are compared. Considering the influence of emergency resource warehouse location on repair time during fault repair, the location of the resource warehouse is determined through spectral clustering to improve repair efficiency. At the same time, the average headway time is introduced to quantify the impact of extreme weather on traffic, modify the road resistance model, and then use the minimum road method to establish an emergency recovery strategy that considers the effects of extreme weather. The IEEE-123 node system is used as an example to verify the rationality of the resilience recovery strategy and the resilience assessment with communication impact.

Social media-based urban disaster recovery and resilience analysis of the Henan deluge.

Measuring disaster resilience from the perspective of long-term recovery ability is important for the planning and construction of urban sustainability, whereas short-term resilient recovery can better reflect a city's ability to recover quickly after a disaster occurs. This study proposes an analytical framework for urban disaster recovery and resilience based on social media data that can analyze short-term disaster recovery and assess disaster resilience from the perspectives of infrastructure and people's psychological states. We consider the downpour in Henan, China, in July 2021. The results show that (1) social media data can effectively reflect short-term disaster recovery, (2) disaster resilience can be assessed using social media data combined with rainfall and damage data, and (3) the framework can quantitatively reflect the differences in disaster recovery and resilience across regions. The findings can facilitate better decision-making in disaster emergency management for precise and effective post-disaster reconstruction and psychological intervention, and provide references for cities to improve disaster resilience.

Shaping a resilient future in response to COVID-19

Science today defines resilience as the capacity to live and develop with change and uncertainty, which is well beyond just the ability to ‘bounce back’ to the status quo. It involves the capacity to absorb shocks, avoid tipping points, navigate surprise and keep options alive, and the ability to innovate and transform in the face of crises and traps. Five attributes underlie this capacity: diversity, redundancy, connectivity, inclusivity and equity, and adaptive learning. There is a mismatch between the talk of resilience recovery after COVID-19 and the latest science, which calls for major efforts to align resilience thinking with sustainable development action. The concept of resilience, once meaning the ability to ‘bounce back’ to the status quo, now refers to the capacity to live and develop with change. A mismatch between the latest science of resilience and the talk of resilience recovery after COVID-19 requires resilience thinking to be aligned with sustainable development.

A major flood caused by a typhoon did not affect the population genetic structure of a river mayfly metapopulation.

Floods affect the population structure of organisms that inhabit streams. In recent decades, the scale of floods has become larger due to climate change. Under these circumstances, on 12 October 2019, the largest typhoon in the history of observation in Japan struck the Japanese Archipelago. This typhoon caused heavy rainfall in various places, and the Chikuma-Shinano River System (Japan's largest) suffered great damage. Eight years before the large-scale disturbance in the river system, the population structure of the mayfly Isonychia japonica was studied in detail using quantitative sampling (population numbers and biomass) and by sequencing the mtDNA cytochrome c oxidase subunit I. To understand the impact of the flood on the population and genetic structures, we repeated the same research approximately 1 year after the flood. Direct comparison of sites before and after the flood revealed no significant changes between pre- and post-flood population genetic structure. This indicates high in situ resistance and/or resilience recovery of the populations to the disturbance. We hypothesize that this high resistance/resilience to flood disturbance is a result of strong selection for such traits in the rivers of the Japanese Archipelago, which are short, steep, flow rapidly and violently, and are strongly affected by floods.

First in-situ monitoring of sponge response and recovery to an industrial sedimentation event

Assessment of risks to seabed habitats from industrial activities is based on the resilience and potential for recovery. Increased sedimentation, a key impact of many offshore industries, results in burial and smothering of benthic organisms. Sponges are particularly vulnerable to increases in suspended and deposited sediment, but response and recovery have not been observed in-situ. We quantified the impact of sedimentation from offshore hydrocarbon drilling over ∼5 days on a lamellate demosponge, and its recovery in-situ over ∼40 days using hourly time-lapse photographs with measurements of backscatter (a proxy of suspended sediment) and current speed. Sediment accumulated on the sponge then cleared largely gradually but occasionally sharply, though it did not return to the initial state. This partial recovery likely involved a combination of active and passive removal. We discuss the use of in-situ observing, which is critical to monitoring impacts in remote habitats, and need for calibration to laboratory conditions.

Wage flexibility and employment resilience in the Spanish labour market over the Great Recession

ABSTRACT The Spanish economy is characterized by significant and persistent regional disparities. The Great Recession caused a severe economic downturn, marked by declining wages and rising unemployment, influenced by the internal wage devaluation policies. We investigate the relationship between wage flexibility and regional labour market resilience. We estimate a spatial panel wage curve using microdata from social security records for the period 2002–19 using geographical and time weighted regression techniques. Our findings reveal that regions with higher wage flexibility exhibit higher resilience recovery indices, highlighting the importance of wage flexibility as a short-term adjustment mechanism in response to labour market shocks.

Air‐Working Electrochemical Actuator and Ionic Sensor Based on Manganese Dioxide/Gelatin‐Glycerol Composites

AbstractSoft materials with large actuation and strain sensing are highly sought after for robotic applications. There are several electrochemical material systems capable of both functions, but as they are developed into air‐working systems, the deformation is compromised by the electrolyte packaging so that they actuate less than in liquid environments. This study develops a novel air‐working film‐actuator by compositing manganese dioxide, which actuates by electrochemical redox reactions, and gelatin‐glycerol hydrogel, which serves not only as the electrolyte for the manganese dioxide but also provides strain sensing function. By properly designing the compositions of the materials and structure, the composite film can produce large bending actuation. Depending on the application and structure adopted, the actuator can produce a tip displacement equal to its own length, a bending angle of >360°, or a radius of curvature of 2.5 mm, which is a very large actuation compared to other air‐working film actuators. Different bending modes or shapes can be achieved by the independent actuating hinges fabricated on the actuator, and strain sensing function and resilient recovery from external compression are achievable by the hydrogel. With these properties, the material system is used to fabricate a robotic inchworm to demonstrate its capability in robotic applications.

Probabilistic recovery resilience model of mooring failure accident based on correlated schedule-uncertainty analysis

Floating production system (FPS) may face various risks during their service life, among which mooring failure is one of the most disastrous events. In order to assess and quantify the resilience of FPS under mooring failure, it is essential to use reliable restoration fragility models. But for now, there are no available specific recovery models. The main barrier is uncertainty of nature and complexity of recovery actions. This paper aims to take uncertainty of recovery schedule into account and propose recovery models for mooring failure accident. Based on literature review and a detailed questionnaire that elicits knowledge from experts, the restoration schedule and uncertainty are defined and quantified. Through correlated schedule uncertainty analysis model (CSUAM), uncertainty influence on recovery model is measured. And after Monte Carlo Simulation (MCS), Gaussian mixture model (GMM) and normal distribution model are introduced to establish probabilistic recovery model of different damage level. Recovery models are then presented and validated for a certain FPS in South China Sea, including restoration task scheduling, seasonal influence, spare availability and two-mobilizations for different damage levels.