Disaster Hit Research Articles

The relationship between disaster resilience and household food security in a disaster-prone area in Kumamoto prefecture, Japan

This study explores the relationship between residents’ disaster resilience and potential household food security in the context of natural disasters. Disaster resilience capacity consists of absorptive capacity, adaptive capacity, and transformative capacity, while household food security is composed of food availability, accessibility, and utilization. Based on data from 539 questionnaires administered to residents in Kumamoto Prefecture, Japan, this study examines households’ disaster resilience capacity and food security conditions. The entropy method is adopted as a quantitative assessment approach to integrate the data, and a Tobit model is constructed to detect the correlation between households’ disaster resilience capacity and food security. We draw five main findings from the results. (1) Over half of the respondents do not have good food security; moreover, food accessibility is the poorest dimension, as reflected by low scores for water purification capacity and facility preparedness. (2) Most of the respondents do not have high disaster resilience capacity; their transformative capacity is the lowest, followed by absorptive and adaptive capacity. (3) There is a significant positive correlation between disaster resilience and household food security. (4) Disaster damage experience restrains residents’ food utilization. (5) The elderly and senior population may be exposed to food-borne diseases because of their low food utilization. This study provides insights into the influence of disaster resilience activities on household food security before a disaster hits. The study informs the debate on the association between disaster resilience and household food security so as to aid future disaster risk reduction management.

Energy efficient gateway based routing with maximized node coverage in a UAV assisted wireless sensor network.

Ad-hoc wireless sensor networks face challenges of optimized node deployment for maximizing coverage and efficiently routing data to control centers in post disaster events. These challenges impact the outcome for extending the lifetime of wireless sensor networks. This study presents a uav assisted reactive zone based EHGR (energy efficient hierarchical gateway routing protocol) that is deployed in a situation where the natural calamity has caused communication and infrastructure damage to a major portion of the sensor network. EHGR is a hybrid multi layer routing protocol for large heterogeneous sensor nodes (smart nodes, basic nodes, user handheld devices etc.) EHGR is tailored to meet two important concerns for a disaster hit wsn ie. optimized deployment and energy efficient routing. The first part of EGHR focuses on maximized coverage during node deployments. Maximized coverage is an important aspect to be considered during the event of disaster since most of the nodes loose coverage and are detached from the wireless sensor network. The first part of EHGR uses state of the art game theory approach to build a model that maximizes the coverage of nodes during the deployment phase from all participating entities i.e. nodes and uavs. Rather than fixing the cluster head as is the case in traditional cluster-based approaches EHGR uses the energy centroid nodes. Energy centroid nodes evolve on the basis of aggregated energy of the zone. This approach is superior to simply electing cluster head nodes on the basis of some probability function. The nodes that fail to achieve any successful outcome from the game theory matching model fail to get any association. These nodes will use multi hop d2d relay approach to reach the energy centroid nodes. Gateway relay nodes used with the game theory approach during the deployment of the uav assisted wsn improves the overall coverage by 25% against traditional leach based hierarchical approaches. Once the optimum deployment phase is completed the routing phase is initiated. Aggregated data is sent by the energy centroid nodes from the ECN nodes to the servicing micro controller enabled un manned aerial vehicles. The routing process places partial burden of zone formation and data transmission to the control center for each phase on the servicing uavs. Energy centroid nodes engage only in the data aggregation process and transmission of data to servicing uav. Servicing-uavs reduce energy dissipated of the entire zone which result in gradual decrease of energy for the zone thus increasing the network lifetime. Node deployment phase and the routing phase of EHGR utilize the computations provide by the mirco controller enabled unmanned aerial vehicles such that the computationally intensive calculations are offloaded to the servicing uav. Experiment results indicate an increase in the first dead node report, half dead node report, and last dead node report. Network lifetime is extended to approximately 1800 rounds which is an increase by ratio of 100% against the traditional leach approach and increase by 50% percent against the latest approaches as highlighted in the literature.

Human Caring at Different Stages After Disasters

Disasters, including natural disasters and man-made ones, occur rather frequently in recent years. Disasters bring destruction to the resources and expectations of local residents and cause varying degrees of physical and mental damage to the victims, some of whom suffered persistent post-traumatic stress disorder (PTSD). Human caring is the essence and core of nursing. Giving consideration to the needs and wishes of patients, the nursing staff respect the personal values of patients, give patients sincere attention and care, satisfy the reasonable needs of patients, protect the dignity of patients, stimulate patients' positive inner drive, alleviate their physical and mental pain, and facilitate the smooth recovery of patients. However, very few studies have been done to look into the human caring at various stages after a disaster hit and there is little relevant guidance available. Herein, we examined the characteristics and needs of victims of disasters in the post-disaster response stage and recovery stage and elaborated on specific and feasible measures for the implementation of human caring after disasters, including prehospital emergency care, which includes on-the-site emergency care and transportation of patients, in-hospital treatment, which includes an environment of human caring and human caring for patients and their families, especially for the special-needs populations, and post-hospital recovery stage, which includes post-hospital continuing and community-based human caring. We intend to provide guidance and reference for the practice of human caring when major disasters occur.

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.

Evolution of Multidimensional Poverty in Crisis-Ridden Mozambique

Mozambique experienced important reductions in the poverty rate until recently, before two major natural disasters hit, an armed insurgency stroke in the northern province of Cabo Delgado, and the country started suffering from a hidden debt crisis with associated economic slowdown. As the last available national household expenditure survey is from 2014/15, just before these crises started unfolding, there is need for a poverty assessment based on alternative data sources. We study the evolution of multidimensional poverty in Mozambique using survey data from the Demographic and Health Surveys (DHS). Using both the standard Alkire–Foster multidimensional poverty index and the first-order dominance (FOD) method, we find that the multidimensional poverty reduction trend observed between 2009–11 and 2015 halted between 2015 and 2018. Meanwhile, the number of poor people increased, mainly in rural areas and in the central provinces. Importantly, the poorest provinces did not improve their rankings over time, and between 2015 and 2018, no progress took place for most areas and provinces, as measured by the FOD approach.

Critical Location of Communications Network with Power Grid Power Supply

When a disaster hits a network, network service disruptions can occur even if the network facilities have survived and battery and power generators are provided. This is because in the event of a disaster, the power supply will not be restarted within the lifetime of the battery or oil transportation will not be restarted before running out of oil and power will be running out. Therefore, taking a power grid into account is important. This paper proposes a polynomial-time algorithm to identify the critical location C*D of a communications network Nc when a disaster hits. Electrical power grid Np supplies power to the nodes of Nc, and a link in Nc is disconnected when a node or a link in Nc or Np fails. Here, the disaster area is modeled as co-centric disks and the failure probability is higher in the inner disk than the outer one. The location of the center of the disaster with the greatest expected number of disconnected links in Nc is taken as the critical location C*D.

Using Strychnos potatorum Seeds as a Natural Coagulant in Flood Affected Areas of Bangladesh

The aim of this research is to find and show the effectiveness of Strychnos potatorum seeds as a natural coagulant for purifying and producing potable water in flood prone rural areas of Bangladesh. Strychnos potatorum, locally called ‘Nirmali’ or ‘Nirmala’, is a natural coagulant which was used in the Indian Subcontinent during the stone ages but has slowly become forgotten. This research was carried out to reintroduce this ancient coagulant and find its effectiveness during floods, particularly in Bangladesh. As a riverine country prone to flooding and devoid of quality water purification systems, coagulants like alum and ferric sulfates are often used although these synthetic chemicals have been found producing DBPs in water which are associated with certain health hazards. And so, replacing these synthetic chemicals with a natural coagulant such as Strychnos potatorum seeds in flood prone rural areas will be a beneficial and cheap water purification system for the populace. To prove the effectiveness, various tests (pH, HCO3 , Cl, Na, K, Na2 CO3 , NTU, TCU, Total Hardness (CaCO3 ), BOD, COD, CFU) were carried out across flood prone areas around Dhaleshwari River (Nagarpur, Tangail) to find out whether Strychnos potatorum seeds will be able to replace the commonly used chemical coagulants and provide drinkable water to the flood affected people or not. Experiments were carried out to find the optimal dosage, flocculation and settling time and reusability of the Strychnos potatorum seeds. The reason behind selecting this region for research purpose is that the soil and tree characteristics of this area is perfect for planting and growing Strychnos potatorum trees and they can be easily distributed among the populace when disaster hits. The test results proved that the parameters comply with the WHO Drinking Water Standards. So, our test results have shown that the Strychnos potatorum seeds are highly effective when it is used to treat water in these flood prone areas. And our initial estimation is Strychnos potatorum will provide a remarkably successful and high yield in these targeted areas. According to our calculations and research it will cost only 0.18 dollars or 18 cents versus 0.25 dollars for most conventional method (Potash Alum) for per liter of water treatment. As the project will require planting Strychnos potatorum trees, the project will improve the environment and distributing them to flood affected populace will increase the peoples’ awareness regarding tree plantation and drinking safe water. Also, this project will act as a replacement to the synthetic chemical coagulants used in the flood affected rural areas of Bangladesh and replacing those synthetic chemical coagulants with biodegradable Strychnos potatorum will be good for the environment as a whole there will be less chemical waste. This project will strengthen the water security of Bangladesh as well as play an important role in stopping the climate change and its adversities.

Developing Indicators to Improve Safety and Security of Citizens in Case of Disruption of Critical Infrastructures Due to Natural Hazards—Case of a Snowstorm in Finland

The changing climate inflicts ecological, economic, social, and cultural consequences that are interconnected and mutually reinforcing. Very often, this happens via interlinked critical infrastructures. Preparing these for natural hazards and carrying out risk assessments that consider their cascading effects on human livelihoods and well-being is a challenging task. Crisis management institutions can benefit from forecasts based on the idea of systemic risk. This study is based on stakeholder workshops, in which a systemic dynamic modelling method called the Causal Loop Diagram (CLD) was used to support contingency planning to identify the critical infrastructure-related factors, the vital functions in society, and to understand their interrelated nature. Together with the workshop participants (authorities and other service providers of critical infrastructures) we tested whether the CLD tool could help identify three types of indicators (threat factors, vulnerability, and resilience) that can help in assessing the risk level when a natural disaster hits. Our case study was a snowstorm, still a frequent phenomenon in the Nordic countries. This article describes and explains the possibilities and limitations of systemic dynamic modelling in contingency planning. Indicators describing the safety and security risks posed by natural hazards, as well as potential sources of data for these indicators, were identified. Identifying indicators that are relevant for anticipating interrelated and cascading effects offers valuable tools for risk assessment and security planning at operational and strategical levels.

Charging Station Distribution Optimization Using Drone Fleet in a Disaster

A disaster is an unforeseen calamity that can cause damage to properties and can bring about a loss of human lives. Usually, many relief supplies, such as clean water, food, and medical supplies, are required by disaster victims. Quick response and rapid distribution of essential relief items into the affected region can save countless lives and prevent or slow down the effects of disasters. In this regard, disaster management comes into play, which is highly dependent on the topography and access of the disaster-hit area. If the disaster-hit site has little or no road connectivity, the use of UAVs/drones becomes essential in delivering health packages to the affected areas to assist with humanitarian aid. Since the battery capacity of the drone is limited, UAVs/drones require charging stations located at various places to carry out the necessary relief work. These charging stations should be transported using road infrastructure and preinstalled in disaster-prone areas, as access to these areas may be denied once the disaster hits. This article presents a novel optimization model to distribute relief items to disaster-hit areas. The objective of this model is to optimize the location and the number of the charging stations. We consider the relative priority of locations where a preference is given to locations with higher priority levels. The optimal number of charging stations and optimal routes has also been determined by using our optimization model. To illustrate the use of our model, numerical examples have been simulated for a different number of targets. Through our numerical simulation, it was observed that the drone’s maximum distance capacity is the key factor in determining the optimal grid size, which directly correlates to the number of charging stations.

Charging Stations Distribution Optimization using Drones Fleet for Disaster Prone Areas

A disaster is an unforeseen calamity that causes damage to property or brings about a loss of human life. Quick response and rapid distribution of vital relief items into the affected region could save precious lives. In this regard, disaster management comes into play, which is highly dependent on the topography of the disaster-hit area. If the disaster-hit area has little or no road connectivity, the use of drones in such areas becomes essential for the delivery of health packages. Since the battery capacity of the drone is limited, there is a need of charging stations that should be transported using road infrastructure and pre-installed in disaster-prone areas, as access to these areas may be denied once the disaster hits. In this article, a simulation model was used to optimize the number and location of drone charging stations for deployment in a disaster-prone area in the pre-disaster scenario, aiming at the distribution of relief items to disaster-hit areas in the post-disaster scenario. We consider the relative priority of locations where a preference is given to the locations that have higher priority levels. An optimal number of charging stations and optimal routes have also been determined by using our optimization model. To illustrate the use of our model, numerical examples have been simulated for different sizes of the disaster-hit area and the number of targets. In our numerical simulation, it was observed that the drone's maximum distance capacity is the key factor in determining the optimal grid size, which directly correlates to the number of charging stations.

Learning in the Digital Age During the COVID-19 Pandemic for School Improvement

Background: Digital learning became increasingly popular when the COVID-19 disaster hit. Even though, its implementation is still less popular after no special indicators are used to create systematic learning. Comprehensive access to online education with fifth generation (5G) mobile technology coverage will enhance the quality of learning in the face of the digital age. Contribution: The contribution of this study is to increase the awareness of online learning during the COVID-19 pandemic. It recognizes the importance of strengthening online learning when schools close. It also highlights digital technologies and practices for school improvement and enters an endemic era. Method: Synthesis method. Researcher synthesized information naturally to help others see connections between things. Results: The presence of the COVID-19 threat has opened a new norm in pedagogical methods in Malaysia. The use of technology has long been championed since 2013. Its importance became apparent after the emergence of the pandemic in early 2020. Not because of pandemic had to give way to technology but the times need change according to the currents of modernity and high technology. Since then, we have faced the threat of a pandemic and through a life of new norms. The convenience of computers and smartphones has changed the current learning process. Conclusion: The COVID-19 pandemic has affected the structure of Learning and Teaching in Malaysian educational institutions. Education institutions were also instructed to close and the live or face-to-face method of Learning and Teaching was changed to fully online Learning and Teaching. The education system will undergo changes of learning in the digital age. Digital learning also continues to be the new norm of pedagogy in the future.

Immunized Token-Based Approach for Autonomous Deployment of Multiple Mobile Robots in Burnt Area

Collaborative exploration, sensing and communication in previously unknown environments with high network latency, such as outer space, battlefields and disaster hit areas are promising in multi-agent applications. When disasters such as large fires or natural disasters occur, previously established networks might be destroyed or incapacitated. In these cases, multiple autonomous mobile robots (AMR) or autonomous unmanned ground vehicles carrying wireless devices and/or thermal sensors can be deployed to create an end-to-end communication and sensing coverage to support rescue efforts or access the severity of damage. However, a fundamental problem is how to rapidly deploy these mobile agents in such complex and dynamic environments. The uncertainties introduced by the operational environment and wide range of scheduling problem have made solving them as a whole challenging. In this paper, we present an efficient decentralized approach for practical mobile agents deployment in unknown, burnt or disaster hit areas. Specifically, we propose an approach that combines methods from Artificial Immune System (AIS) with special token messages passing for a team of interconnected AMR to decide who, when and how to act during deployment process. A distributed scheme is adopted, where each AMR makes its movement decisions based on its local observation and a special token it receives from its neighbors. Empirical evidence of robustness and effectiveness of the proposed approach is demonstrated through simulation.

Uttarakhand Flash Floods February 2021: Perils of Under-Preparedness or Disregard for the Environment or Both

Disaster hit Uttarakhand’s Chamoli district on February 7, 2021, in the form of sudden flash floods after a portion of the Nanda Devi glacier was broke apart. The sudden floods were reported in the intricately linked tributaries of the Ganga River - Dhauli Ganga, Rishi Ganga, and Alaknanda rivers which triggered widespread panic and large-scale devastation and fatalities. The Flash floods damaged two power projects - NTPC’s Tapovan-Vishnugad Hydel Project and the Rishiganga Hydel Project, five bridges, roads, and settlements, in and around Raini, Tapovan and Joshimath areas in Uttarakhand. Scores of laborers working at the sites of the Hydel Projects were trapped and feared to have lost lives, despite all our efforts to rescue them. It is the second large-scale tragedy in the same area within a span of fewer than eight years. Questions are being asked,”Why did we not learn any lesson from the earlier disaster in the very same area”? Was it under-preparedness, or was its disregard to the environment or both? This paper attempts to find the right answers to the questions and suggest solutions so that in the future, such avoidable disasters do not happen.

NATURAL DISASTER: ITS IMPLICATION TO HUMAN SECURITY

This study is intended to provide an overview of a natural disaster that significantly affects human security, especially in Malaysia. Furthermore, it enhances the understanding of natural disaster determinant factors and improves the knowledge about the risk, vulnerability, and consequences of a natural disaster. Despite the complexity of human security, this conceptual study can provide the platform for the Malaysian Government to assess the implication of natural disasters and protect human security. Therefore, it is crucial in handling human needs. Such approaches require well-coordinated and quick responses from government and non-government agencies. Furthermore, safety measures or precautions should be pre-planned for human security before the disaster hits.

Emerging Energy Harvesting Materials and Devices for Self‐Powered Water Disinfection

Contaminated drinking water is one of the main pathogen transmission pathways making waterborne illnesses such as diarrheal diseases and gastroenteritis a huge threat to public health, especially in the areas where sanitation facilities and gird power are inadequate such as rural and disaster hit areas. Self-powered water disinfection systems are a promising solution in these cases. In this review paper, the authors provide an overview of the new and emerging methods of applying energy harvesting materials and devices as a source of power for water disinfection systems microbial disinfection in water by harnessing ambient forms of energy such as mechanical motion, light, and heat into electricity. The authors begin with a brief introduction of the different energy harvesting technologies commonly applied in water disinfection; triboelectric, piezoelectric, pyroelectric, and photovoltaic effects. Various microbial disinfection mechanisms and types of device construction are summarized. Then, a detailed discussion of the energy harvester-driven water disinfection process is provided. Finally, challenges and perspectives regarding the future development of self-powered water disinfection are described.

Coconut trees detection and segmentation in aerial imagery using mask region‐based convolution neural network

Food resources face severe damages under extraordinary situations of catastrophes such as earthquakes, cyclones, and tsunamis. Under such scenarios, speedy assessment of food resources from agricultural land is critical as it supports aid activity in the disaster hit areas. In this article, a deep learning approach is presented for the detection and segmentation of coconut tress in aerial imagery provided through the AI competition organized by the World Bank in collaboration with OpenAerialMap and WeRobotics. Maked Region-based Convolutional Neural Network approach was used identification and segmentation of coconut trees. For the segmentation task, Mask R-CNN model with ResNet50 and ResNet1010 based architectures was used. Several experiments with different configuration parameters were performed and the best configuration for the detection of coconut trees with more than 90% confidence factor was reported. For the purpose of evaluation, Microsoft COCO dataset evaluation metric namely mean average precision (mAP) was used. An overall 91% mean average precision for coconut trees detection was achieved.

An integer L-shaped algorithm for the integrated location and network restoration problem in disaster relief

Being prepared for potential disaster scenarios enables government agencies and humanitarian organizations to respond effectively once the disaster hits. In the literature, two-stage stochastic programming models are commonly employed to develop preparedness plans before anticipated disasters. These models can be very difficult to solve as the complexity increases with several sources of uncertainty and interdependent decisions. In this study, we propose an integer L-shaped algorithm to solve the integrated location and network restoration model, which is a two-stage stochastic programming model determining the number and locations of the emergency response facilities and restoration resources under uncertainty. Our algorithm accommodates the second-stage binary decision variables which are required to indicate undamaged and restored roads of the network that can be used for relief distribution. Our computational results show that our algorithm outperforms CPLEX for the larger number of disaster scenarios as the solution time of our algorithm increases only linearly as the number of scenarios increases.

La reconstrucción de cocinas de bahareque cerén a partir de los saberes tradicionales de las comunidades de Oaxaca, México

En el año 2017 un terremoto asoló diversas comunidades de las regiones rurales de Oaxaca. Esta catástrofe afectó de manera especial a las mujeres productoras de totopos, quienes perdieron sus cocinas y hornos, sus herramientas esenciales de trabajo y subsistencia. Cooperación Comunitaria A.C. ha llevado a cabo un proyecto de reconstrucción que, al utilizar técnicas constructivas tradicionales, especialmente la técnica del bahareque cerén, ha involucrado en su desarrollo a la población local. Esta actuación ha permitido no sólo la recuperación de las condiciones materiales y económicas de las distintas comunidades afectadas, sino también el intercambio de saberes y experiencias.

Hurricane impact associated with transactional sex and moderated, but not mediated, by economic factors in Okay, Haiti.

Natural disasters, such as hurricanes, often negatively affect the economic wellbeing of affected individuals. Under such conditions, women may engage in transactional sexual behaviors in order to compensate for lost income and provide for their households. In this study, we evaluated the relationship between hurricane impact and transactional sex and if this relationship was moderated, or mediated, through economic factors. Between December 2017 and February 2018, approximately one year after the area was hit by a category 4 hurricane (Hurricane Matthew), we interviewed a random sample of female microfinance members (n=304) in Okay, Haiti. We estimated the association between hurricane impact and transactional sex using log-binomial regression. Next, we tested for economic moderation of this relationship by incorporating interaction terms between hurricane impact and food insecurity, poverty, and loss of income generating materials in three separate log-binomial models. Finally, we assessed possible mediation of this relationship by loss of income generating resources and a latent variable, economic stress, using a structural equation model. We found participants who had experienced hurricane impact were 58% more likely to have engaged in transactional sex [prevalence ratio (95% confidence interval): 1.58 (1.19-2.09)]. This relationship was significant and strongly positive among women who were food insecure and had high poverty but not so among women who were food secure and had low poverty, respectively. The final structural equation model (with fit: χ2=19.700, degrees of freedom=15; CFI=0.958; TLI=0.941; RMSEA=0.040) revealed that the relationship was not mediated through economic factors. However, the association between hurricane impact and transactional sex remained significant while controlling for these economic factors. Notably, the findings that the relationship between hurricane impact and transactional sex was moderated, but not mediated, by economic factors implies populations at-risk of experiencing natural disasters should be preemptively targeted with economic interventions to build capacity and resilience before such a disaster hits.

Roundtable on the Pandemics of Racism, Environmental Injustice, and COVID-19 in America

Roundtable on the Pandemics of Racism, Environmental Injustice, and COVID-19 in America