Improve Disaster Resilience Research Articles

Management of natural disasters in the environment PT. Mutiara Masyhur Sejahtera, with the formation of an emergency response team

Background: Sidoarjo Regency, East Java, is often hit by tornadoes that cause material losses and injuries, especially at PT. Mutiara Masyhur Sejahtera located at Jalan Kahuripan Raya No. 9-11, Entalsewu, Buduran, Sidoarjo. This phenomenon encourages the need to improve disaster preparedness and response. Method: This study uses a qualitative descriptive approach with primary data collection through direct observation and in-depth interviews with the company's Project Manager. Secondary data covering organizational structure and number of employees are also used. Data is analyzed through a reduction process to ensure relevant and accurate information. Findings: Although PT. Mutiara Masyhur Sejahtera already has fire protection equipment, such as Light Fire Extinguishers (APAR), but there is no system or policy that regulates emergency response management for fires or other disasters. Forming an Emergency Response Team and creating Procedures and Work Instructions (IKA) regarding Emergency Response are needed as a reference when an emergency occurs. Conclusion: To improve disaster preparedness, companies must equip themselves with an occupational safety and health management system (SMK3) that regulates all compliance related to K3 aspects. This aims to prevent work accidents and occupational diseases, reduce the negative impact of disasters and improve overall work safety. Novelty/Originality of this study: This study presents a new approach to improving disaster preparedness in the private sector, especially for companies operating in disaster-prone areas such as Sidoarjo. This study proposes the integration of an occupational safety and health management system (OHSMS) with a particular focus on disaster emergency response management, which can be a model for other companies in improving disaster resilience.

A Capability Maturity Model for Benchmarking in Wargames

This research provides an analysis of maturity models, and insights from specific game studies such as unclassified non-kinetic games, supported by contributions from the wargaming community. By proposing a design framework inspired by capability maturity models used in software development, cyber security, and people management, this research introduces a new benchmark for evaluating wargames, in a reproducible and standardised fashion. This model facilitates the identification of strengths and areas for improvement, offering a structured path to higher maturity levels. It aims to enable wargame designers to assess and compare wargame components systematically, enhancing the ability to validate outcomes, predict gameplay effects, and support decision-making with greater confidence. Such advancements could significantly impact policies and improve disaster resilience, particularly within Defence strategy and capabilities, marking a significant advancement in the academic and practical enhancement of the wargaming field.

When, at what speed, and how? Resilient transformation of the Vesdre river basin (Belgium) following the 2021 floods

BackgroundDual crises happen when an acute shock unfolds in the context of a creeping crisis. The July 2021 floods in the Vesdre river basin (Wallonia, Belgium) is a typical case of such dual crises in the context of climate change. This study is based on 16 semi-structured interviews (conducted in Spring 2023) with 10 mayors, 4 representatives of the Public Service of Wallonia, 1 person working for the federal government, and 4 project managers, coupled with a document analysis (n = 13). It investigates the temporal strategies that connect short and long-term considerations in the aftermath of this disaster (timing, futuring, pacing, cyclical adaptation, and determining time horizons), at two different governmental levels: river basin and municipal level.ResultsIn general, the window of opportunity to improve disaster resilience has been seized. Several studies were initiated by the Walloon region that shape the idea of an ideal future for the river basin and give recommendations for how to reach it. Unfortunately, those recommendations still come late compared to the temporal reality of the reconstruction process. Municipalities wish to strengthen disaster resilience as soon as possible, but they have to prioritize certain actions over others because of limited resources. The recommendations are considered flexible enough to adapt strategies to future contexts, but no monitoring and evaluation system for doing so has been implemented so far. In addition, clear policy agendas with transformational goals are scarce, and they diverge between the river basin and the municipalities. All these temporal strategies are shaped by elements of the institutional policy arrangement: resources, which affect them all, as well as actors, power, and formal rules, which affect some. These policy dimensions notably slow down the implementation of disaster resilience strategies and limit the determination of consensual time horizons.ConclusionsThe temporal strategies are passively shaped by the policy arrangement dimensions to a greater extent than actively chosen by the stakeholders. A structural transformation of the institutional policy arrangement is therefore needed to enable more coherent temporal strategies between different governance levels and to facilitate the consideration of long-term resilience during the recovery process from disasters.

A SURVEY ON CONTINUOUS POSTGRADUATE DISASTER MEDICINE EDUCATION NEEDS IN THE PLOVDIV REGION

Introduction: Disasters, regardless of their origin, have a direct negative impact on the physical and psychological wellbeing of the population. Medical support for the casualties necessitates specific organization and training of medical personnel. Knowledge and skills in the field are considered to be essential for the execution of a rapid response. Purpose: The aim of this research was to study the need for postgraduate disaster medicine education for different categories of medical professionals in the Plovdiv region. Materials and methods: A cross-sectional study was conducted in 2020 through an anonymous inquiry among medical professionals from pre-hospital healthcare, hospital care and the centers for emergency medical aid in the Plovdiv region. Results: The survey was held among 160 medical professionals. They were interviewed regarding their participation in medical support for disasters or mass casualty incidents. 50,3% of them had such experience. 65% of the respondents evaluated their knowledge of disaster medicine as moderate, while only 18,7% reported high self-assessment. Most interviewees knew the knowledge and skills needed for disaster medical support, but half had never trained in these skills. Almost all respondents - 93,1%, considered disaster medicine knowledge useful for medical practice. 89,4% share the opinion that postgraduate courses were necessary. 93,2% preferred to be trained and educated on the subject by the Medical University. Conclusions: The results from our survey highlight the lower than expected self-confidence among medical professionals regarding their disaster medical support knowledge and skills. Postgraduate training could improve disaster resilience by increasing skill and confidence.

Socio-economic vulnerability level in the Jeneberang watershed in Gowa Regency, South Sulawesi Province, Indonesia

Jeneberang watershed is vital, particularly for people living in Gowa Regency (South Sulawesi Province, Indonesia), who benefit from its many advantages. Landslides and floods occur every year in the Jeneberang watershed, so it is imperative to understand the socio-economic vulnerability of this region. This research aims to identify the vulnerability level of the Jeneberang watershed so that the government can prioritize areas with high vulnerability level and formulate effective strategies to reduce these the vulnerability. Specifically, this study was conducted in 12 districts located in the Jeneberang watershed. The primary data were collected from questionnaires completed by community members, community leaders, and various stakeholders, and the secondary data were from the Landsat satellite imagery in 2020, the Badan Push Statistic of Gowa Regency, and some governmental agencies. The socio-economic vulnerability variables were determined using the Multiple Criteria Decision Analysis (MCDA) method, and each variable was weighted and analyzed using the Geographical Information System (GIS). The study reveals that the levels of socio-economic vulnerability are affected by variables such as population density, vulnerable groups (disabled people, elderly people, and young people), road network and settlement, percentage of poor people, and productive land area in the Jeneberang watershed. Moreover, all of the 12 districts in the Jeneberang watershed are included in the medium vulnerability level, with the mean percentage of socio-economic vulnerability around 50.92%. The socio-economic vulnerability of Bajeng, Pallangga, and Somba Opu districts is categorized at high level, the socio-economic vulnerability of Bungaya, Parangloe, and Tombolo Pao districts is classified as medium level, and the remaining 6 districts (Barombong, Bontolempangan, Bontomarannu, Manuju, Parigi, and Tinggimoncong) are ranked as low socio-economic vulnerability. This study can help policy-makers to formulate strategy that contributes to the protection of biodiversity and sustainable development of the Jeneberang watershed, while improving disaster resilience and preparedness of the watershed.

Priority for improving disaster resilience at the Higher Education Level: awareness, integration, and Management

Abstract Muhammadiyah University (Unismuh) Palu, one of the universities in Central Sulawesi, has potential resources to ensure the use of science and technology foundations in disaster management. In addition, increasing disaster resilience is a shared responsibility and requires the involvement of all parties, including the academic community, through systematic, massive, and structured activities. The research aims to analyze the priority of increasing disaster resilience at the Higher Education Level based on the role of internal stakeholders and the determinant aspects related to awareness, integration, and management. The study used a qualitative descriptive method to describe the history of earthquake, tsunami, and liquefaction events in Central Sulawesi. The Analytical Hierarchy Process (AHP) method is used to determine the hierarchy of stakeholder priorities and aspects in increasing disaster resilience at Unismuh Palu through implementing the Four Pillars of Higher Education towards a Disaster Awareness Academic Community and a Disaster Preparedness Campus. The results show that disaster literacy is very important in efforts to increase disaster resilience at Unismuh Palu. The results show that disaster literacy is very important in efforts to increase disaster resilience at Unismuh Palu. Institutional leadership and awareness are a priority for the role of internal university stakeholders and the determining aspect that has the highest value. This is related to regulations and management that guarantee the implementation of disaster mitigation integrated with the Four Pillars of Higher Education activities to increase disaster resilience at Unismuh Palu.

Locked into a permanent position of vulnerability? Farmers’ trust and social capital with the government from Critical Disaster Studies perspectives

AbstractIn disaster scholarship, there is limited understanding about how vulnerability is socially constructed by a convergence of varying social structural and historical processes that predispose certain vulnerable groups to disasters even before a hazard event occurs. This article aims to fill this void by using Critical Disaster Studies perspectives to explore the root causes of why crop farmers in the coastal region of the Vietnamese Mekong Delta have been locked in a permanent position of vulnerability. In order to develop more sustainable food supply chains, this exploration aims to shed light on the socio-cultural interpretations, experiences, and needs of disaster-affected farmers—the most important but vulnerable actors in the crop production hierarchy in Vietnam. Following a historic disaster between late 2015 and early 2016, local governments deliberately organised farmers into an agricultural cooperative in an attempt to support farmers in enhancing economic resilience and disaster preparedness. However, this intervention has failed since farmers refused to participate in the organisation’s activities. While this reaction may seem irrational, it reflects farmers’ distrust in the government, which was largely a result of their collective trauma from the state’s agricultural collectivisation policy implemented between 1976 and 1985. Farmers chose to cling to their individual working routines out of concern about government supervision, forcing them to take bigger risks and pay for the food supply chains’ catastrophic damage. Our findings show that a strained relationship between farmers and the government can jeopardise disaster recovery and resilience building. Our paper adds to the Critical Disaster Studies literature by revisiting the concepts of vulnerability and resilience, emphasising the importance of addressing socio-cultural vulnerability before improving disaster resilience.

Preferences for index-based crop insurance in South Africa.

The results are expected to contribute to the local government to improve disaster resilience through strengthening regional financial funding.

Improving disaster resilience: a common goal for the EU, insurers and researchers

Improving disaster resilience: a common goal for the EU, insurers and researchers

Understanding memory transmission in disaster risk reduction practices: A case study from Japan

“Disasters strike when they have faded from memory”, is a powerful quote from the Japanese geophysicist Terada Torahiko (1878–1935). This sentence, often used when addressing the need to improve disaster resilience, is a powerful reminder of the role that local memories of past disasters play in ensuring the safety of communities exposed to hazards. Twelve years from the Great East Japan Earthquake and Tsunami (GEJET) that hit Japan Tohoku's East Coast, this article aims at understanding how the transmission of local knowledge and experiences of past disasters contributes to increasing community resilience to tsunami risks. Based on field research along the coast of the Japanese prefectures of Iwate, Miyagi and Fukushima, this study analyses 11 semi-structured in-depth interviews, to investigate the role of memory transmission in preparing for tsunami risks such as the GEJET. The data was analysed following a narrative approach and through thematic coding with the use of NVivo. Coding structures led to the identification of disaster memory transmission practices in the case study areas and allowed the testing of the relevance of the theoretical framework to community disaster resilience studies. Results are aligned with the theoretical framework and show that disaster memorials are more likely to influence community resilience if passed down using a combination of oral transmission and interpretation together with the use of backup memory archives and physical objects. Such practices are recommended as they allow both the communicability and durability of lessons-learned. Transmitting and maintaining a constant interaction between these two forms of memory is the responsibility of those institutions that oversee the keeping alive of memories, by continuously repurposing and renewing them while engaging with exposed communities.

Enhancing hurricane risk perception and mitigation behavior through customized virtual reality

Hurricanes are one of the most destructive natural disasters, and proper preparation and mitigation are essential to enhance resilience. However, homeowners may have inaccurate risk perceptions, resulting in inadequate mitigation decision-making. Virtual reality (VR) damage visualization has the potential to communicate hurricane risk effectively and improve disaster resilience. Previous studies utilizing VR for hurricanes have primarily focused on disaster response such as evacuation, and often presented generalized visualization consistent across all users. This study presents a methodology for offering individual users a customized tool to experience the impacts of hypothetical hurricane events on their residential structures and explore the effects of various building retrofit actions in customized 3D virtual environments. The methodology was tested with 25 participants. Participants experienced structural damage and utility disruptions caused by hurricanes in their own residential homes in a customized, immersive virtual environment. Participants’ pre- and post-VR risk perceptions, as well as their choices of mitigation actions were surveyed. The results indicate that customized VR was effective in enhancing risk awareness and that participants were able to reduce their structural damages in hypothetical hurricane events by adopting various mitigation measures after the customized VR experience. The outcomes suggest that customized visualization of potential damages in virtual environments can effectively help disaster preparation and mitigation.

Distributionally robust optimization for collaborative emergency response network design

The post-disaster emergency response capacity of a single country is limited, and a collaborative approach that pools emergency resources can improve disaster resilience. In this paper, we study a multi-country collaborative emergency response network design problem with uncertain demand and transportation time. Considering the benefits of inter-regional cooperation in sharing emergency facilities and resources, we construct a collaborative emergency response network (CERN) design framework. The cost of CERN is allocated among the partner countries according to their expect standalone response cost and the level of economic development. In practice, the distribution information of random parameters is not perfectly known, so we propose a distributionally robust optimization (DRO) model to design the CERN. A scenario-wise ambiguity set is constructed to characterize the uncertain parameters based on disaster-level-related events. To solve the proposed DRO model, we propose a decomposition-based algorithm with a valid inequality. In the numerical study, we first verify the advantage of the CERN design approach. The proposed scenario-wise DRO method is subsequently compared with alternative modeling approaches to assess its out-of-sample performance. The findings confirm the efficacy of the constructed ambiguity set in capturing the uncertainty stemming from varying magnitudes of catastrophic events. The computational study demonstrates that the proposed algorithm exhibits superior computational efficiency compared to the commercial solver CPLEX for large-scale problems. Additionally, we conduct sensitivity analyses on various parameter configurations and provide managerial insights for the CERN design problem.

Role of tourism on disaster recovery: A case study of Uttarakhand, India

The Indian Himalayan Region (IHR) is a tectonically active region, making it susceptible to natural and man-made hazards. The Himalayan state of Uttarakhand has a history of disasters that cause huge loss of life and property every year. Tourism plays an imperative role in the state's economy due to its natural resources and pilgrimage sites. To foster tourism, the Government of India proposed the Char-Dham National Highway project to connect the four major pilgrimage sites with other tourist destinations. However, development projects related to tourism, such as road construction, blasting, muck disposal, mining, deforestation, etc., may even increase the frequency of disasters. Hence, this study examines how tourism contributes to disaster impact and recovery. A survey of 716 households was conducted in 32 villages in Uttarakhand where major disasters happened in the past decade. Using the Mann-Whitney U test, differences between households on on-touristic and on off-touristic routes are highlighted. The findings revealed that people in both categories have suffered the same harsh effect of disasters, but their socioeconomic conditions are significantly different. Overall, disaster recovery in off-touristic route villages appears to be slower than in on-touristic route villages because of a lack of livelihood opportunities, alternative income sources, poverty, and a lack of disaster coping skills. A special recovery assistance is needed for off-touristic route villages as they are more vulnerable to hazards. Development projects related to tourism seem to facilitate disaster recovery, but still are connected with considerable disaster impacts. Thus, developing sustainable tourism solutions with resilient planning is an important step in improving disaster resilience of hill communities.

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.

The wisdom of crowds for improved disaster resilience: a near-real-time analysis of crowdsourced social media data on the 2021 flood in Germany

Transformative disaster resilience in times of climate change underscores the importance of reflexive governance, facilitation of socio-technical advancement, co-creation of knowledge, and innovative and bottom-up approaches. However, implementing these capacity-building processes by relying on census-based datasets and nomothetic (or top-down) approaches remains challenging for many jurisdictions. Web 2.0 knowledge sharing via online social networks, whereas, provides a unique opportunity and valuable data sources to complement existing approaches, understand dynamics within large communities of individuals, and incorporate collective intelligence into disaster resilience studies. Using Twitter data (passive crowdsourcing) and an online survey, this study draws on the wisdom of crowds and public judgment in near-real-time disaster phases when the flood disaster hit Germany in July 2021. Latent Dirichlet Allocation, an unsupervised machine learning technique for Topic Modeling, was applied to the corpora of two data sources to identify topics associated with different disaster phases. In addition to semantic (textual) analysis, spatiotemporal patterns of online disaster communication were analyzed to determine the contribution patterns associated with the affected areas. Finally, the extracted topics discussed online were compiled into five themes related to disaster resilience capacities (preventive, anticipative, absorptive, adaptive, and transformative). The near-real-time collective sensing approach reflected optimized diversity and a spectrum of people’s experiences and knowledge regarding flooding disasters and highlighted communities’ sociocultural characteristics. This bottom-up approach could be an innovative alternative to traditional participatory techniques of organizing meetings and workshops for situational analysis and timely unfolding of such events at a fraction of the cost to inform disaster resilience initiatives.

Wind design of solar panels for resilient and green communities: CFD with machine learning

Climate change mitigation and adaptation in urban environments call for more reliance on clean energy sources. Large photovoltaic (PV) systems have been enjoying renewed interest in clean and renewable energy. However, designing resilient PV systems faces an increased risk due to windstorms. Whether wind loads on PV systems are well understood, properly accounted for, and the damage is mitigated are crucial questions. While computational fluid dynamics (CFD) is proven effective for quantifying wind loads on structures, accurate and affordable computations are challenging. In this paper, we employ CFD approaches and machine learning (ML) to obtain the design wind loads on solar panels. We validate the CFD simulations using experimental data and compare the results with the standard practice. Our findings suggest that experimentally validated CFD simulations can yield different results from the standard practice. Additionally, we recommend stowing solar panels at a -15° angle during wind events to reduce damage. CFD simulations are then employed to train an ML model to predict velocity and pressure distributions around a solar panel. The study demonstrates that integrating ML and CFD can significantly speed up simulations (up to 10,000 times faster) without sacrificing accuracy. Efficient designs can shape the future of PV systems and contribute to climate change adaptation and mitigation for improved disaster resilience and circular economy policies.

Disaster risk index on disaster management budgeting: Indonesia's national data set.

The results are expected to contribute to the local government to improve disaster resilience through strengthening regional financial funding.

Vibration Attenuation in a High-Rise Hybrid-Timber Building: A Comparative Study

Recent developments in engineered timber products, and their availability, durability, and renewability, have led to taller and more flexible buildings. However, these buildings may experience excessive vibrations, resulting in safety and serviceability issues due to wind or earthquake loads. This paper presents a dynamic analysis of a 42-story-tall hybrid-timber building, along with a comparative study of the performance of three damping devices: (i) pendulum pounding tuned mass damper (PTMD), (ii) tuned mass damper inerter (TMDI), and (iii) tuned mass damper (TMD). First, we evaluate the vibration reduction capability of the TMD and the TMDI under filtered white noise and variable frequency sinusoidal excitations. Then, we propose a robust pendulum PTMD designed using the Hertz contact law to minimize the responses under seismic excitations. For a fair comparison, the mass of the TMD, TMDI, and pendulum PTMD is kept the same. The results show that the pendulum PTMD has higher performance and can reduce the peak accelerations under earthquake loads when both TMD and TMDI fail to achieve this requirement. The superior performance of the proposed device in reducing peak accelerations relates to the reduction in damage to structural and nonstructural components under seismic loads. Nevertheless, coupling the inerter and TMD to form a TMDI may shift the optimum frequency and damping ratios, leading to reduced performance. Compared to TMD and TMDI, the proposed pendulum PTMD is more robust, with higher performance in reducing the base shear (55.7%), base moment (41%), and inter-story drift ratio (40%). The dominant capabilities of this novel device in a timber-hybrid building under different excitations reveal benefits that can shape the future of the physical infrastructure and contribute to climate change adaptation and mitigation for improved disaster resilience and circular economy policies.

Sudden-onset disaster resilience considering functionality improvement planning: An upstream oil and gas company

The present paper presents a quantitative approach to evaluate organizational resilience in sudden-onset disasters considering preparedness actions (PAs). The resilience triangle concept is extended and gradual improvement of the level of functionality (LF) strategy is examined as PAs. Robustness and rapidity measures are considered as loss of LF and recovery time to indicate the overall disaster resilience. Besides, the resourcefulness and redundancy measures are considered pre-determined response plans and PAs aimed to improve disaster resilience. Two mathematical models are developed by applying these measures. Thereafter, graphical analytics are conducted to demonstrate the effectiveness of PAs and provide a better outlook for decision-makers. Then, mathematical analyses are conducted to show how the PAs affect resilience measures. It is mathematically proved that PAs exert considerable impacts on recovery time, loss of LF, and robustness. To illustrate the application of the proposed approach, it is applied to an upstream oil and gas company in the field of exploration and production. The results suggest that the approach is significantly effective in disaster response, planning, and mitigation.

Research on the application of neural network based external location element settlement method in object location of geographic information

BackgroundGeographical information about human settlements is necessary to facilitate humanitarian aid, recognize the location of real-world objects, support local development and improve disaster resilience in settlements. Although deep learning is employed in object location in settlements, these models have computational complexity and less detection accuracy. Hence they require further development with regards to precision in object location from geographic data. ObjectiveTo efficiently identify the object location in Chinese settlements from geographic information, we proposed Artificial Neural Network (ANN) optimized by Red Fox (RF) optimization (ANN-RF) model in this research. MethodsThe multi-resolution Geographic information data was collected from Tongzhou District, Beijing, China. ANN applies the measures of length, area, distance and direction to recognize object location from this data. An RF algorithm is used to optimize the weight vector of the ANN architecture to improve the efficiency of the ANN architecture. The parameters such as precision, recall, F1-score, number of correct detections, overall actual matches and the total number of detections were computed for the proposed model. ResultsThe precision rate and F1-score for the proposed ANN based RF optimization are about 98.9% and 97.5% which are higher than that of conventional models. ConclusionThe proposed ANN-RF model shows promising results in detecting object location in settlements from geographic information.