Post-disaster Management Research Articles

Benefiting from Energy-Hub Flexibilities to Reinforce Distribution System Resilience: A Pre- and Post-Disaster Management Model

The proliferation of power-to-gas technology can propound a tailored platform to physically integrate power systems and natural gas grids. These integrated energy systems with different spatial-temporal properties not only could provide significant flexibilities to properly mitigate existing and imminent challenges, but also could increase the robustness of power systems in facing unpredicted conditions. Keeping this in mind, this article outlines a novel conservative two-stage model to improve the resilience of distribution systems against extreme hurricanes. To this end, at the first stage, a pre-disaster scheduling is executed to increase preparedness and robustness of the power system before approaching the tornado. The preparedness index is defined as the sum of energy stored in the electric vehicles and natural gas storages that should be maximized. Subsequently, at the second stage after the recognition of the tornado, some proactive post-disaster actions such as grid partitioning, network reconfiguration, demand-side management, and distributed series reactors are applied to minimize the degradation and vulnerability of the power system. An integrated gas and electricity power flow is proposed in a linear computationally efficient fashion capable of modeling the worst-case scenario. The effectiveness of the model is examined on a distribution grid with multiple energy hubs.

Prevalence and Predictors of Generalized Anxiety Disorder Symptoms in Residents of Fort McMurray Five Years after the Devastating Wildfires

Background: Natural disasters adversely impact individuals living in places where they occur, resulting in emotional distress. The wildfire that occurred in Fort McMurray (FMM), Alberta in 2016 is no different. Objective: This study aims to identify the prevalence and predictors of Generalized Anxiety Disorder (GAD) symptoms in residents of FMM five years after the devastating wildfires. Methods: Data for the study were collected through a cross-sectional survey conducted online from the 24th of April to the 2nd of June 2021. A validated instrument, the GAD-7 scale, was used to collect information on anxiety. Results: This study involved 186 residents of FMM, of which the majority were females (85.5%), employed (94.1%), working at school boards (50.0%), and were either married, cohabiting, or partnered (71.0%). The prevalence of likely GAD among the study sample was 42.5%. Unemployed respondents were seventeen times more likely to develop GAD symptoms (OR = 16.62; 95% C.I. 1.23–223.67) while respondents who would like to receive mental health counseling were five times more likely to experience GAD symptoms (OR = 5.35; 95% C.I. 2.03–14.15). Respondents who suffered a loss of property because of the wildfire were two times more likely to develop GAD symptoms (OR = 2.36; 95% C.I. 1.01–22.62). Conclusion: Policymakers may mitigate GAD symptoms, particularly after natural disasters, by making long-term mental health counseling available and a key component of post-disaster management, and by investing in the social capital of the people to build resilience and support to deal with the post-disaster mental health effects.

Deducing Flood Development Process Using Social Media: An Event-Based and Multi-Level Modeling Approach

Social media is increasingly being used to obtain timely flood information to assist flood disaster management and situational awareness. However, since data in social media are massive, redundant, and unstructured, it is tricky to intuitively and clearly obtain effective information. To automatically obtain clear flood information and deduce flood development processes from social media, the authors of this paper propose an event-based and multi-level modeling approach including a data model and two methods. Through the hierarchical division of events (division into spatial object, phase, and attribute status), the flood information structure (including time, space, topic, emotion, and disaster condition) is defined. We built an entity construction method and a development process deduction method to achieve the automatic transition from cluttered data to orderly flood development processes. Taking the flooding event of the Yangtze and Huai Rivers in 2020 as an example, we successfully obtained true flood information and development process from social media data, which verified the effectiveness of the model and methods. Meanwhile, spatiotemporal pattern mining was carried out by using entities from different levels. The results showed that the flood was from west to east and the damage level was positively correlated with the number of flood-related social media texts, especially emotional texts. In summary, through the model and methods in this paper, clear flood information and dynamic development processes can be quickly and automatically obtained, and the spatiotemporal patterns of flood entities can be examined. It is beneficial to extract timely flood information and public sentiments towards flood events in order to perform better disaster relief and post-disaster management.

Psychometric properties of the Disaster Preparedness Evaluation Tool (DPET) Thai version among Thai registered nurses

Nurses are the largest professional group in healthcare, and play a significant role in disaster management. Knowledge about nursing skills in disaster preparedness is needed to improve understanding and provide appropriate interventions for the nursing workforce. However, there is no standardized tool to evaluate disaster preparedness among Thai nurses. This study therefore aimed to translate the Disaster Preparedness Evaluation Tool (DPET) into Thai and examine the psychometric properties of the translated version. The study had two phases. Phase 1 consisted of the back-translation and monolingual test for cross-cultural aspects. Phase 2 consisted of expert testing for internal consistency and content validity, and exploratory factor analysis. Cluster random sampling and stratified random sampling were used to provide a sample of 770 registered Thai nurses to complete the instrument.Content validity was assessed by five experts, resulting in a good content validity index. Cronbach's alpha coefficient for the total scale was 0.973. The item analysis showed that 45 items had an acceptable internal consistency using the corrected item–total correlations. One item was deleted. Construct validity was determined using exploratory factor analysis. It found six factors: post-disaster management, disaster skills, knowledge of preparedness, knowledge specific to response, acquiring disaster education, and sources of disaster knowledge. These factors explained 67.134% of the total variance. The psychometric properties indicated that the Thai version of the DPET had adequate reliability and validity to evaluate the level of disaster preparedness among Thai nurses. The instrument should be tested further to ensure standardization.

A novel attention-based deep learning method for post-disaster building damage classification

Although several past studies proposed deep learning methods to extract pre-disaster building footprints for post-disaster management using remote sensing techniques, building damage classification after a disaster is rarely discussed. Building damage classification using deep learning is a challenging task because imagery data of damaged buildings are limited publicly and damage levels are hard to judge from images. Most building damage classification papers with deep learning methods only judge buildings as collapse or not. Moreover, most deep learning models are designed for non-damaged building segmentation, which might not be suitable for damaged building segmentation. In order to solve the problems, including lack of imagery data, detailed damage levels, and suitable deep learning model for damaged building segmentation, this research aims to propose and evaluate a novel deep learning model to classify building damage into four levels quickly with in-house labelled damaged building information. This research proposes the Squeeze-and-Excitation dual High-Resolution Network (HRNet) model to be trained with open access xBD dataset and the 2010 Haiti Earthquake images labelled at the University of New South Wales. HRNet is adopted twice in the model because two steps need to be completed, building localization and then damage classification. Four comparative experiments are implemented with seven metrics, including combination F1, localization F1, localization precision, localization recall, damage F1, damage precision, and damage recall. The first experiment is to choose where the best place is to add SE channel attention block in the model. The Squeeze-and-Excitation (SE) block is added at four different places in the dual HRNet backbone to have a comparison with the model without SE. According to analyses of the results, the SE-PRE model has the best performance. The second experiment shows that a larger input size of images increases the processing time but performs better. The third and fourth experiments reveal that the Sigmoid function is better than Hard-Sigmoid in SE, and the transfer learning with pre-trained ImageNet weights may not be suitable for the proposed model. The theoretical contribution is proposing a novel deep learning model for building damage classification. Another contribution of this research is creating a Haiti Earthquake image dataset with four building damage levels. The practical contribution is providing a safe and speedy post-disaster building damage classification method with minimal fieldwork to support rescue teams.

Penataan Dokumen Pasca Bencana Berbasis Data Digital

Post-disaster management is the last stage in the disaster management cycle which aims to restore the lives of people who have experienced disasters so that they can restart socio-cultural activities and economic efforts to improve their welfare. To achieve one goal requires cooperation supported by various parties. Building communication with others, in this case internal stakeholders, is very necessary in order to achieve a common perception. The success of a team in achieving its goals is measured by the level of work effectiveness of team members. The higher the level of goal achievement, it can be said that the team is effective. Work effectiveness is influenced by the division of labor among team members. The cross-sectoral coordination function is very necessary for the achievement of the organization's mission and the achievement of other tasks, including how to provide support for the Action Plan for Changes in the Arrangement of Post-Disaster Documents Based on Digital Data, so that this can run not only as a formality of completeness of training but can be a continuous reference for reliability. storage or arrangement of documents that are free from disaster damage. The role of stakeholders in achieving the objectives of an activity must be based on how much influence these stakeholders have in contributing to the desired goals. Analysis of stakeholder involvement, both internal and external, must be carried out by looking at the authority that is carried out for the smooth running of all activity processes.

A method to assess the value of monitoring an SHM system

Abstract Aging structural components, together with the increasing transportation needs and limited budgets, are challenging aspects that typically concern decision-makers and infrastructure owners. Although Structural Health Monitoring (SHM) has been a powerful tool to optimize maintenance-related activities and post-disaster emergency management, the sensor readout and, therefore, the outcome of the monitoring system is susceptible to errors due to malfunctioning. For years, the Value of Information (VoI) has been studied to quantify the long- term benefit of SHM systems against the initial investment in sensing instrumentation without considering the eventuality of faulty sensing nodes. However, these are very common in field applications. This paper proposes a new framework to calculate the benefit of using Sensor Validation Tools (SVTs) before calculating the damage-sensitive features that drive the SHM process. The novel approach extends the traditional Vol to consider multiple “health” states of the SHM system, associate the outcome of the SHM system with the state of both the structure and the SHM system, and quantify the additional value obtained from SVTs.

A simulation-based decision support tool for dynamic post-disaster pedestrian evacuation

Taiwan, located in the Pacific Ring of Fire, is highly vulnerable to the destructive impact of powerful earthquakes. When a massive earthquake occurs, failure to take proper measures can lead to massive casualties, collapse of public facilities, and disruption to traffic networks. Among the many post-disaster management strategies, efficient evacuation is absolutely critical as it can potentially reduce the number of casualties substantially. At present, most post-disaster evacuation decisions are made based on past experience, but the rarity of high-magnitude earthquakes impedes the rate at which the quality of the decision-making process can improve. As such, this study primarily aims to develop an efficient and credible evacuation simulation modeling framework which can be used as a powerful tool to bolster evacuation strategies. The simulation evacuation framework we propose is known as the Stochastic Pedestrian Cell Transmission Model (SPCTM). The model is built based on real data of the mapping of roads and emergency shelters in an urban environment, as well as predictive models of pedestrian flow, the number of people needed to be evacuated after an earthquake, and the impact of earthquakes on urban infrastructure. Much of the data is provided by the Taiwan Earthquake Impact Research and Information Application platform (TERIA) developed by the National Science and Technology Center for Disaster Reduction (NCDR). Applying this data, the model divides the traffic network into a cellular structure, applies the concepts of inflow and outflow to represent population movement and uses random variables to simulate the selection behavior of pedestrian agents. In addition to SPCTM, we propose an alternative model which makes slight modifications to SPCTM to offer users a complementary framework in which evacuees are armed with less ideal information about the evacuation situation. An empirical study is run in a district of Taipei, Taiwan based on a simulated earthquake of magnitude 6.5 in which the overall and moment-by-moment simulation evacuation process is presented. The use of SPCTM as a simulation evacuation model is compared to the current government evacuation policy in Taiwan. Further potential benefits of using the proposed simulation evacuation tool are numerous. For example, the frameworks proposed in this study can offer valuable insights to policy-makers on infrastructure design decisions and evacuation strategies before an earthquake occurs, including the best placement of relief centers, to potentially significantly reduce the number of casualties. Also, simulation results can be utilized after a disaster to assist in crucial decisions related to road restoration priorities, distribution of supplies to shelters, etc.

Policymakers' perspectives on responding to the elderly's mental health needs in post-disaster situations.

Background: Mental health issues following the occurrence of disaster remain neglected area especially for older people group. The purpose of this study was to explore stakeholders’ perspectives on post-disaster management related to the elderly’s mental health needs.Design and methods: This qualitative case study was conducted between June and October 2020. The investigators conducted in-depth interviews with policymakers who were in charge of disaster management at national, provincial, and regional levels. The policymakers were selected through purposive sampling. A policy analysis was conducted by the investigators to answer the research question.Results: The results were presented based on actor, content, context, and process. The actors engage in intersectoral collaboration between disaster agencies, health agencies, and social agencies. The content is largely comprehensive; however, the disaster management policy should not neglect to address mental health conditions after a disaster event. The context is the vulnerability and risk of the elderly in terms of experiencing physical and mental issues after a disaster, which should be considered by policymakers in Indonesia. The process is the development of disaster management policies, which are influenced by cultural, economic, political, and international factors.Conclusions: This study reveals that stakeholders need to pay attention to the mental health issue of elderly in national and regional policy, particularly during post-disaster situations. In addition, posttraumatic stress in post-disaster situations should be highlighted in order to formulate a better aged care policy. Significance for public health This study analyzes stakeholders’ perspectives on improving post-disaster mental health aged care policy in Indonesia. This research provides information and policy recommendations for the government to promote coordination at the national and regional levels and the establishment of comprehensive post-disaster programs for the elderly.

Modeling of Direct Economic Losses of Storm Surge Disasters Based on a Novel Hybrid Forecasting System

Modeling the direct economic losses of storm surge disasters can assess the disaster situation in a timely manner and improve the efficiency of post-disaster management in practice, which is acknowledged as one of the most significant issues in clean production. However, improving the forecasting accuracy of direct economic losses caused by storm surge disasters remains challenging, which is also a major concern in the field of disaster risk management. In particular, most of the previous studies have mainly focused on individual models, which ignored the significance of reduction and optimization. Therefore, a novel direct economic loss forecasting system for storm surge disasters is proposed in this study, which includes reduction, forecasting, and evaluation modules. In this system, a forecasting module based on an improved machine learning technique is proposed, which improves the generalization ability and robustness of the system. In addition, the key attributes and samples are selected by the proposed reduction module to further improve the forecasting performance from the two innovative perspectives. Moreover, an evaluation module is incorporated to comprehensively evaluate the superiority of the developed forecasting system. Data on the storm surge disasters from three typical provinces are utilized to conduct a case study, and the performance of the proposed forecasting system is analyzed and compared with eight comparison models. The experimental results show that the mean absolute percentage error (MAPE) predicted by the Extreme Learning Machine (ELM) model was 16.5293%, and the MAPE predicted by the proposed system was 1.0313%. Overall, the results show that the performance of the proposed forecasting system is superior compared to other models, and it is suitable for the forecasting of direct economic losses resulting from storm surge disasters.

Infrastructure design and multi disasters-based land use as the main key to Palu resilience city

Indonesia is located at the meeting point of three tectonic plates, making it at-risk to geological disasters such as volcanic eruptions and earthquakes. Earthquake disaster can cause a variety of crucial dangers, such as earthquakes, landslides, liquefaction, tsunamis, and other natural disasters, as one took place in Palu City in 2018. This study aims to identify key parameters that could increase Palu resilience in the face of calamity in the future. This study start with a literature review to determine the concept, parameters, and variables of resilience Palu City against disasters. These parameters and variables were then compared to the current condition. The findings of the literature review generated four main parameters for disaster-resilience cities: disaster risk reduction (improvement of infrastructure design and multi disasters-based land use planning), community recovery, efficient program implementation, and monitoring-evaluation. Furthermore, the comparison of these four parameters to reconstruction activities reveals that Palu city’s post-disaster reconstruction has not fully resulted in efforts to achieve the concept of a disaster-resilience city. This is because post-disaster management efforts in Indonesia are in accordance with applicable regulations, emphasizing recovery rather than mitigation in the following catastrophic events.

POST-DISASTER EDUCATION MANAGEMENT: AN ANALYTICAL STUDY OF PERMENDIKBUD NUMBER 33 2019 CONCERNING THE IMPLEMENTATION OF THE DISASTER SAFE EDUCATION UNIT PROGRAM

Natural disasters have an impact on educational, economic, social, political aspects, even at the religious level of the community. Some of the main factors causing many victims due to disasters are the lack of public knowledge about disasters, thus causing a lack of preparedness in anticipating the disaster. This research is a literature research, which aims to find a post-disaster management format based on Permendikbud Number 33 of 2019 concerning the Implementation of the Disaster Safe Education Unit Program. This research is motivated by the lack of socialization of the Minister of Education and Culture Number 33 of 2019 in educational institutions in Indonesia which causes schools to be unprepared in dealing with disasters. School unpreparedness further increases vulnerability to disasters. The research method uses content analysis and comparison. The results of this research analysis lead to a conclusion that school residents must be literate in disaster education as an effort to reduce disaster risk. This can be done through the following steps: 1) realizing structurally and non-structurally safe schools 2) incorporating disaster education into subject matter either through integration or separate materials, as well as intra and extra-curricular activities, 3) collaborating with the health office, BNPB , Search and Rescue, NGOs and institutions related to post-disaster management.

Diversity-based Approach in Post Disaster Management

Disasters cause marginalization, inequality and vulnerability caused by power relations, resulting in diversity in the community. This article aims to describe diversity in disasters as well as practical competence through a diversity-based approach. The research method uses a literature review with a descriptive approach to describe diversity in disasters. The results of the study concluded that diversity in disasters can be divided into several categories, namely women, the elderly, people with disabilities, children and adolescents and people with mental disorders. Diversity-based approach competence through cultural competence needs to be carried out in implementing post-disaster management interventions.

Nursing Students' Perceived Disaster Preparedness and Response: Pilot study in Oman.

ObjectivesThis study aimed to assess the perceived level of disaster preparedness and response among nursing students in Oman, as the country is disaster prone and experiences disasters every three to five years.MethodsA descriptive cross-sectional pilot study was conducted from May to June 2019 using a stratified cluster sampling method among nursing students recruited from two government nursing colleges in Muscat, Oman. The Disaster Preparedness Evaluation Tool and Disaster Response Self-Efficacy Scale were used to assess the knowledge, skill, disaster management and self-efficacy in handling disasters.ResultsA total of 51 students participated in this study. Most students (78.4%) had experienced a disaster while at home. Overall, the students had moderate levels of knowledge (3.17 ± 1.49), skill (3.12 ± 1.52), post-disaster management (3.22 ± 1.44) and self efficacy in responding to disaster (2.93 ± 1.16).ConclusionNursing students in Oman have experienced disasters and are willing to respond when called upon though they possess moderate knowledge and confidence in handling disasters.

Mining Dam Failures in Brazil: Comparing Legal Post-Disaster Decisions.

Mining dam failures have increased worldwide since the 1980s. Two large mining dam failures occurred recently in Mariana and Brumadinho, both in the state of Minas Gerais, Brazil. We hypothesize that there were significant differences in legal post-disaster decisions. The aim of this article is to understand the similarities and differences of post-disaster actions and controversies in Mariana and Brumadinho. We reviewed 686 news reports about court decisions and settlement agreements from the websites of state and federal courts and judicial institutions. After classifying the reports using an adapted protocol from a media health observatory, we conducted a thematic analysis. Our analysis suggests that there were significant differences in legal post-disaster decisions in the cases of Mariana and Brumadinho. In Mariana, there was privatization of post-disaster management, with the creation of the Renova Foundation, a mediated indemnity program, lack of access to information for those affected, and uncertainties in health and resettlement issues. In Brumadinho, there was faster implementation of the recovery and compensation measures, faster recognition of affected parties, and stronger participation of the population since the first hearings. Even though there were particularities in post-disaster management, the ultimate goal of the corporations responsible for the disasters was to protect their profits.

Fairness in ambulance routing for post disaster management

Disaster management generally includes the post-disaster stage, which consists of the actions taken in response to the disaster damages. These actions include the employment of emergency plans and assigned resources to (i) rescue affected people immediately, (ii) deliver personnel, medical care and equipment to the disaster area, and (iii) aid to prevent the infrastructural and environmental losses. In the response phase, humanitarian logistics directly influence the efficiency of the relief operation. Ambulances routing problem is defined as employing the optimisation tools to manage the flow of ambulances for finding the best ambulance tours to transport the injured to hospitals. Researchers pointed out the importance of equity and fairness in humanitarian relief services: managing the operations of ambulances in the immediate aftermath of a disaster must be done impartially and efficiently to rescue affected people with different priority in accordance with the restrictions. Our research aim is to find the best ambulance tours to transport the patients during a disaster in relief operations while considering fairness and equity to deliver services to patients in balance. The problem is formulated as a new variant of the team orienteering problem with hierarchical objectives to address also the efficiency issue. Due to the limitation of solving the proposed model using a general-purpose solver, we propose a new hybrid algorithm based on a machine learning and neighbourhood search. Based on a new set of realistic benchmark instances, our quantitative analysis proves that our algorithm is capable to largely reduce the solution running time especially when the complexity of the problem increases. Further, a comparison between the fair solution and the system optimum solution is also provided.

Study on hidden danger investigation and impact assessment method for earthquake-induced secondary disasters in coal mines

Mining is one of the key production industries that involves hazardous operations and various safety risks, and is prone to being seriously affected by earthquake disasters and secondary disasters. Indeed, following a number of earthquakes, including the Tangshan and Wenchuan earthquakes, serious secondary disasters, such as tunnel engineering damage and the rapid increase of water inflow, occurred, resulting in varying degrees of casualties and economic losses. In this study, with the problem of the lack of investigation and impact assessment methods for earthquake-induced secondary disasters in coal mines, an appropriate investigation system was designed based on the theories of earthquake disaster science, safety engineering, and system science while considering the mine lifeline system as the main line. Based on the analysis methods for fault type, effect analysis, and multi-index synthesis, a comprehensive evaluation method for the impact of earthquake-induced secondary disasters in coal mines was proposed. The method involves the following steps. First, according to the historical information on regional earthquake disasters, the situation of mine earthquake disaster fortification, and the risk of earthquake disasters, the possibility of earthquake-induced secondary disasters in coal mines is evaluated according to the complexity of mine occurrence conditions. Second, the severity of the consequences of secondary disasters induced by an earthquake is evaluated in terms of three aspects: casualties, economic losses, and disaster prevention and mitigation ability. Finally, the risk grade of the secondary disasters induced by the earthquake is determined by constructing a risk grade matrix of a mine disaster bearing body. The evaluation method covers numerous factors, including the current situation in terms of pre-disaster preparedness, the level of the earthquake disaster, and the ability of the post-disaster management to offer emergency disaster reduction and relief, thus achieving full coverage of all the key influencing factors before, during, and after the disaster. Using the proposed evaluation method, the quantitative expression of the impact assessment of earthquake-induced secondary disasters in coal mines was realized, which provides a reference for the quantitative assessment of the impact of earthquake disasters on the coal mining industry.

Analysis of Pre- and Post-Disaster Management and Recovery in Puerto Rico from Hurricane Maria

Special Issue, Volume 19, Number 8

Effect of virtual reality simulation training on the response capability of public health emergency reserve nurses in China: a quasiexperimental study

ObjectiveTo develop a virtual reality simulation training programme, and further verify the effect of the programme on improving the response capacity of emergency reserve nurses confronting public health emergencies.DesignA prospective...

An Integrated Approach for Post-Disaster Flood Management Via the Use of Cutting-Edge Technologies and UAVs: A Review

Rapid advances that improve flood management have facilitated the disaster response by providing first aid services, finding safe routes, maintaining communication and developing flood maps. Different technologies such as image processing, satellite imagery, synthetic imagery and integrated approaches have been extensively analysed in the literature for disaster operations. There is a need to review cutting-edge technologies for flood management. This paper presents a review of the latest advancements in the flood management domain based on image processing, artificial intelligence and integrated approaches with a focus on post-disaster. It answers the following research questions: (1) What are the latest developments in image processing for flood management in a post-disaster scenario? (2) What are the latest techniques for flood management based on artificial intelligence in a post-disaster scenario? (3) What are the existing gaps in the selected technologies for post-disaster? (4) How can the authorities improve the existing post-disaster management operation with cutting-edge technologies? A novel framework has been proposed to optimise flood management with the application of a holistic approach.