Disaster Relief Efforts Research Articles

How effective crowdsourced data during crisis emergency? A case of the 2018 Palu-Donggala earthquake

In disaster situations, updated geographic data is crucial for disaster relief efforts. OpenStreetMap (OSM) has demonstrated significant value in disaster response scenarios due to its capacity for rapid data collection and dissemination, since the 2010 Haiti earthquake. This study investigates the quality of OSM data during the 2018 Palu-Donggala earthquake, focusing on how contributor expertise affects data reliability and how effectively OSM data supports decision-making in emergencies. The research highlights the critical role of OSM in providing timely geospatial information, with 205 contributors mapping roads and buildings in Palu City and Donggala Regency within just three days of the earthquake. Our findings show that while road data exhibited substantial topological errors—7,085 errors primarily due to overshoots—building data had considerably fewer errors, with only 76 recorded. This disparity suggests that OSM data for buildings was of higher quality during the crisis. The preference of eight out of nine mapper types for building data over road data further underscores the value of OSM in emergencies, as experienced mappers tended to focus on features that were less error-prone. The study also evaluates contributor behavior, revealing that while a significant portion of contributors were inactive, a majority of experienced contributors remained engaged. This finding indicates the potential for inactive expert mappers to return and contribute in future crises. Additionally, the study assesses the rapid collection of data by OSM and its impact on decision-making. The National Disaster Management Agency of Indonesia (BNPB) and the ASEAN Coordinating Centre for Humanitarian Assistance (AHA Centre) effectively utilized the data to provide updates on fatalities, injuries, and displacement, facilitating a swift and equitable distribution of aid.

An integrated systematic method for interlaced unmanned spatial systems (IUSS) design process

Unmanned Aerial Systems (UAS) have garnered significant interest in recent years. These systems, commonly consisting of one or more Unmanned Aerial Vehicles (UAV) and satellite systems, have been extensively used to enhance the effectiveness of various SOSs, such as disaster management and relief efforts. In addition, the inaugural Mars unmanned helicopter, Ingenuity (Ginny), successfully took flight in April 2021, marking the beginning of the utilization of these systems on Mars. This research aims to build an integrated approach for developing Interlaced Unmanned Spatial Systems, considering the high level of precision required for space systems. This study aims to set up and optimize all parts of the proposed architecture for the design of IUSS, using Model-Based Systems Engineering theories and Dependency Structural Matrix foundations. This research introduces a comprehensive coherence architecture that considers all design domains, including the design process, design office, products, and requirements. Additionally, a design workflow model is provided.

Ethics in humanitarian settings-relevance and consequences for dialysis and kidney care.

With the increasing frequency and severity of disasters and the increasing number of patients living with kidney disease, on dialysis and with transplants around the world, the need for kidney care in humanitarian settings is increasing. Almost all humanitarian emergencies pose a threat to kidney health because all treatments are highly susceptible to interruption, and interruption can be deadly. Providing support for people requiring dialysis in humanitarian settings can be complex and is associated with many trade-offs. The global kidney care community must become familiar with the ethics, principles and duties essential to meeting the overarching goals of ethical and effective disaster relief. Ethics principles and values must be considered on the individual, public health and global levels. The wellbeing of a single patient must be considered in the context of the competing needs of many others, and optimal treatment may not be possible due to resource constraints. Public health ethics principles, including considerations of triage and resource allocation, maximization of benefit and feasibility, often become directly relevant at the bedside. Individuals delivering humanitarian relief must be well trained, competent, respectful and professional, while involved organizations need to uphold the highest professional and ethical standards. There may be dissonance between ethical guidance and practical realities in humanitarian settings, which for inexperienced individuals may present significant challenges. Sustaining dialysis care in emergencies brings these issues starkly to the fore. Preparedness for dialysis in emergencies is an ethical imperative that mandates multisectoral stakeholder engagement and action, development of surge response plans, clinical and ethics guidance, and transparent priority setting. This manuscript outlines common ethics challenges and considerations that apply in all humanitarian actions, and illustrates their relevance to kidney care as a whole, using examples of how these may apply to dialysis and kidney disaster relief efforts in humanitarian settings.

Counseling Psychologists Assist Post-Disaster Psychological Rescue Experience in Major Disasters

<p>近年國內多起重大災難事件造成個體與社會群體的影響與不安，諮商心理師身為專業醫事人員提供受災者災後心理救援，也衍生災難事件危機介入的實踐與困境。本研究以現象學的研究方法、半結構式訪談蒐集資料，受訪者為六位諮商心理師，其中三位具督導資格，研究結果包含二類與八個主題：（1）諮商心理師災後心理救援經驗，包含：（a）災後心理救援前的自我評估與專業知能；（b）災後心理救援時的角色與功能；（c）災後心理救援時的預備、初期、中期、中長期作為與督導者作為；（d）災後心理救援後的多重困境、身心壓力與文化民俗挑戰。（2）因應災後心理救援困境的實務規劃，包含（a）落實演練與心理教育；（b）持續增能與案例討論；（c）建立名單與督導關係；（d）熟悉雲端與資訊操作；（e）調整期待與服務心態。作者依研究結果提出災後心理救援「停、看、問」三個步驟與九項建議：（1）「停」—介入之前：（a）自評身心狀態與投身意圖；（b）具足所需專業知能與增能；（c）培育多元文化介入的能力。（2）「看」—介入當下：（a）理解角色功能適才適性；（b）調整不同階段介入作為；（c）理解困境挑戰正面因應。（3）「問」—介入之後：（a）身心自我調適與回歸原有平衡；（b）尋求督導協助重整介入經驗；（c）系所課程培育與繼續教育增能，最後提及未來研究建議。</p> <p> </p><p>Since the ""Taiwan Disaster Year"" began with the 921 earthquake, Taiwan has witnessed numerous significant disaster events in recent years, resulting in extensive financial losses and loss of life. These events have profoundly affected individuals’ trust and sense of security, leading to a widespread impact on social groups and cultures. As professional healthcare providers, counseling psychologists frequently engage in post-disaster psychological rescue and reconstruction efforts during major disasters or crises. This practice has raised challenges for crisis intervention during such events. Researchers thus aim to understand the landscape and challenges of psychological rescue efforts during domestic disasters. This study uses phenomenological research methods and semi-structured interviews to gather data. It includes interviews with six counseling psychologists, three of whom are certified supervisors who acted as supervisors during disaster events, while the other three provided crisis intervention services as psychologists. The study categorizes its findings into two main themes and eight sub-themes: Experiences of counseling psychologists in post-disaster psychological support: (1) Self-assessment and professional knowledge before providing post-disaster psychological support: This theme explores how psychologists evaluate their readiness and expertise before engaging in disaster relief efforts. (2) Roles and functions of post-disaster psychological support: This examines the specific responsibilities and activities undertaken by psychologists during the support process. (3) Preparatory, initial, mid-term, and long-term actions, including the role of supervisors: This delves into the different stages of psychological support and the crucial role of supervisors in guiding and supporting these efforts. (4) Multiple challenges, physical and mental stress, and cultural and folk challenges after providing post-disaster psychological support: This theme addresses the complex challenges psychologists face following their intervention, including stress and cultural issues. Practical planning to address the challenges of post-disaster psychological support: (1) Implementing drills and psychological education: Emphasizes the importance of preparedness through regular drills and education. (2) Continuous capacity building and case discussions: Highlights the need for ongoing training and discussion of cases to enhance skills and knowledge. (3) Establishing lists and supervisory relationships: Focuses on creating structured relationships and lists to streamline support efforts. (4) Familiarity with cloud and information operations: Stresses the need for proficiency in modern technology to facilitate efficient support. (5) Adjusting expectations and service mentality: Discusses the necessity of adapting expectations and maintaining a service-oriented mindset. Based on the research results, the researcher proposes a systematic approach comprising three steps (""Stop, Look, Ask"") and nine specific suggestions for post-disaster psychological support, serving as a practical reference for crisis intervention practice: ""Stop""—before intervention: (1) Self-assess physical and mental state and willingness to engage: Psychologists need to evaluate their readiness and motivation. (2) Equip with necessary professional knowledge and capacity building: Psychologists need to ensure that they have the required expertise and training. (3) Cultivate the ability for multicultural intervention: Develop skills to effectively engage with diverse cultural backgrounds. ""Look""—during intervention: (1) Understand the roles and functions and fit them appropriately: Matching roles and tasks to individuals’ strengths. (2) Adjust intervention actions at different stages: Modify approaches according to the phase of intervention. (3) Understand challenges and respond positively: Recognize and constructively address difficulties encountered. ""Ask""—after intervention: (1) Physical and mental selfadjustment and return to original balance: Focusing on recovery and well-being. (2) Seek supervisory assistance to reorganize intervention experiences: Utilizing supervision to reflect and improve.(3) Curriculum development and continued education for capacity building: Advocating for ongoing education and training in disaster psychology. Finally, the study concludes with recommendations for future research and suggests areas for further exploration to enhance the effectiveness of psychological support in disaster scenarios.</p> <p> </p>

Kegiatan Program Pengabdian Masyarakat Kelompok 073 Berbasis Masjid Di Kelurahan Jenggalu

This community service program, conducted by Group 073 of UINFAS Bengkulu, aimed to address several key issues in Kelurahan Jenggalu, including mosque cleanliness, religious education, and disaster relief. The primary objectives were to enhance mosque facilities, promote religious learning through regular Quranic studies, and provide support to flood victims. The program employed a participatory approach involving activities such as mosque cleaning, Quranic education sessions, a Muharram competition, health exercises, and assistance for flood-affected families. Data was collected through direct observation, questionnaires, and interviews with participants and mosque officials. The results indicated a significant improvement in mosque cleanliness, increased community engagement in religious activities, and positive feedback on disaster relief efforts. The activities successfully fostered greater community involvement and addressed the pressing needs of the local population, demonstrating the effectiveness of mosque-based community programs in enhancing social welfare.

Forecasting and stock control for fast-moving humanitarian aid supplies

In Indonesia, effective disaster management is imperative due to the region's susceptibility to a wide array of natural and man-made disasters. The establishment of the National Disaster Management Agency (BNPB) underscores the significance of this issue. This study investigates the improvement of disaster logistics, with a particular emphasis on the challenges associated with fast-moving humanitarian supplies. The primary obstacle in disaster logistics lies in the unpredictability of demand after the disaster occurred, coupled with the potential for supply disruptions stemming from inadequate inventory in warehouses. A critical focus of this research is placed on fast-moving humanitarian aid supplies, which are essential for immediate relief efforts, even when lead times are short. The study's primary objective is the development of a safety stock level policy for BNPB's national warehouse, adjusted through the analysis of demand forecasts and lead times. Notably, the research results for the year 2022 reveal minimal errors in demand forecasts. This outcome highlights the effectiveness of the safety stock policy in proactively addressing spikes in demand and mitigating uncertainties related to supply timelines. The research underscores the critical role of accurate inventory management, particularly in the case of fast-moving humanitarian supplies, within the context of disaster relief efforts. It emphasizes the importance of forward-thinking planning to meet urgent needs, ultimately contributing to more efficient disaster response efforts and ensuring the timely delivery of vital relief items to affected populations.

A stacking machine learning model for predicting pullout capacity of small ground anchors

Small ground anchors are widely used to fix securing tents in disaster relief efforts. Given the urgent nature of rescue operations, it is crucial to obtain prompt and accurate estimations of their pullout capacity. In this study, a stacking machine learning (ML) model is developed for the rapid estimation of pullout capacity offered by small ground anchors used for temporary tents, leveraging cone penetration data. The proposed stacking model incorporates three ML algorithms as the base regression models: K-nearest neighbors (KNN), support vector regression (SVR), and extreme gradient boosting (XGBoost). A dataset comprising 119 in-situ anchor pullout tests, where the cone penetration data were measured, is utilized to train and assess the stacking model performance. Three metrics, i.e., coefficient of determination (R2), mean absolute error (MAE), and root mean square error (RMSE), are employed to evaluate the predictive accuracy of the proposed model and compare its performance against four popular ML models and an empirical formula to highlight the advantages of the proposed stacking approach. The results affirm that the proposed stacking model outperforms other ML models and the empirical approach as achieving higher R2 and lower MAE and RMSE and more predicted data points falling within 20% error line. Thus, the proposed stacking model holds promising potential as a solution for efficiently predicting the pullout capacity of small ground anchors.

A Geographical Appraisal of Particularly Vulnerable Tribal Groups (PVTGs) of Jharkhand and their Susceptibility to Environmental Hazards

Since time immemorial, tribes and nature have evolved in symbiosis. Any disturbance in nature affects these communities immensely. Environmental risks have become increasingly common over the world, causing tremendous concern for society due to its high level of susceptibility. India, due to its unique climatic and socio-economic conditions, is vulnerable, to various disasters. Though, environmental hazards affect all, its impact is more severe on Particularly Vulnerable Tribal Groups (PVTGs). Measuring sensitivity to environmental threats is a huge challenge for disaster relief efforts worldwide. It is vital to measure the level of vulnerability in order to apply vulnerability reduction and mitigation actions effectively. The aim of the study is to analyze spatial distribution of PVTGs and their susceptibility to environmental hazards of Jharkhand. The study’s main finding revealed that two-third concentration of PVTGs are residing in the north-eastern part of the state. The largest concentration was found in Santhal Pargana and north-eastern part of the Jharkhand. Among all the tribes, Mal Pahariya constituted the largest group of vulnerable tribes followed by Sauria Paharia and Korwa. Birhor community was observed to be scattered in the region. Pakur district was known to have highest PVTG community. A district wise environmental susceptibility revealed that lightning, earthquakes, exposure to heat/stroke and cold had intimidated these tribes due to least socio-economic development. Lack of district wise data have inculcated the inconsistencies in the result. To mitigate the vulnerabilities of PVTGs, disaster risk reduction strategies must be tailored to their specific needs. The development of indigenous knowledge along with techno how can prove worth for community specific resilience towards environmental hazards.

Rescue Relief Agency Management For Disaster Recovery

In the aftermath of a natural or man-made disaster, the swift and coordinated deployment of rescue agencies is critical to minimizing casualties and maximizing the effectiveness of relief efforts. However, the current landscape of disaster response is often hampered by the absence of a centralized platform for communication and collaboration among various rescue agencies. This lack of a unified system often leads to duplication of efforts, inefficient resource allocation, and delays in providing aid to those in dire need. To address these challenges and enhance the efficiency of disaster response, we propose the development of a comprehensive mobile application that would enable rescue agencies to seamlessly register their information, share real-time location updates, and coordinate their operations effectively. This proposed mobile application would serve as a central hub for rescue agencies to register their information, including their location, contact details, areas of expertise, and available resources. This information could be entered manually or automated using GPS or other location tracking technologies. Once the database is populated, the application would provide a user-friendly interface that allows users to visualize the locations of registered rescue agencies on a map. Additionally, users could filter the results based on specific criteria, such as the type of disaster, the resources available, or the time since the last reported activity. Beyond displaying the locations of rescue agencies, the application would also facilitate communication and collaboration among these organizations. Rescue agencies could send alerts or requests for assistance directly through the application, enabling them to coordinate their efforts and optimize resource utilization. Moreover, the application could facilitate the sharing of critical resources such as medical equipment, transportation, and communication infrastructure, ensuring that these resources are deployed where they are most needed. Overall, the proposed mobile application has the potential to revolutionize disaster response by providing a centralized platform for communication, collaboration, and resource management among rescue agencies. By enabling real-time coordination and efficient resource allocation, this application could significantly enhance the effectiveness of disaster relief efforts, saving lives and minimizing the impact of natural and man-made disasters..

The core competencies in disaster nursing of new graduate nurses in Guangdong, China: A cross-sectional study

AimThis study aimed to assess the level of core competencies in disaster nursing of New Graduate Nurses (NGNs) and explore its influencing factors. BackgroundIn recent years, the overall frequency of disasters around the world has been on the rise. As the emerging workforce in clinical settings, NGNs play an integral role in future disaster relief efforts. NGNs’ level and influencing impact of core competencies in disaster nursing need to be understood. DesignA cross-sectional design. MethodsFrom June to September 2023, the Core Competencies in Disaster Nursing Scale - General Professional Nurse (CCDNS-GPN), designed according to the CCDN V2.0, was used to collect data from NGNs of six nursing schools and 15 hospitals in Guangdong, China. Descriptive analysis was conducted to examine the scores of core competencies in disaster nursing. Furthermore, one-way analysis of variance and multivariate linear regression analysis were used to explore the influencing factors. ResultsA total of 607 NGNs participated in this study. The scores of CCDNS-GPN of NGNs were 90.23 (SD 15.09) (score ratio: 51.56%), indicating a low level of core competencies in disaster nursing. The highest competency was the recovery (score ratio: 55.00%), while the lowest competency was the communication (score ratio: 45.44%). The predictor for core competencies in disaster nursing of NGNs were male (β = 0.091, p < 0.05), below bachelor’s degree (β = −0.109, p < 0.05), had received disaster nursing education at school (β = 0.087, p < 0.05), had participated in a disaster rescue drill at school (β = 0.140, p < 0.05), had been to the ICU during internship (β = 0.135, p < 0.05) and had the intention to be a disaster specialized nurse (β = 0.114, p < 0.05). ConclusionsThe NGNs exhibited insufficient core competencies in disaster nursing in Guangdong, China. Nursing schools and clinical institutions should collaborate and play their respective roles to enhance nurses' core competencies in disaster nursing, ensuring they can timely, safely and efficiently participate in disaster medical relief efforts.

China and India in the Indian Ocean: A Study of Strategic Importance and its Influence on Geopolitical Dynamics and Security Policies

This article explores the strategic significance of the Indian Ocean for China and India, examining how their actions influence geopolitical dynamics and security policies. The Indian Ocean, a vital global trade route, has seen increased attention from both nations through their naval expansions, infrastructure investments, and diplomatic engagements. This has sparked debates about potential competition, strategic rivalries, and the implications for regional security. The study aims to understand the impact of China and India's activities in the Indian Ocean and their potential for cooperation or conflict. The research question focuses on how these actions affect regional security. The methodology employed is a qualitative research approach, incorporating a literature review, document analysis, case studies, and a comparative analysis of China and India's strategies in the Indian Ocean. The results highlight the Indian Ocean as a critical geopolitical arena undergoing significant transformations due to the actions of China and India. The rise of these two powers has the potential to either lead to conflict and instability or contribute to regional stability by providing public goods such as anti-piracy patrols and disaster relief efforts.

Humanitarian Logistics and Emergency Response in Humanitarian Organizations in Kenya.

Disasters were unpredictable and unavoidable, necessitating adequate disaster response plans. Humanitarian logistics, which gained prominence after the 2004 Asian tsunami, played an important part in crisis prevention. Despite weak links in relief chain management, issues remained. Humanitarian logistics had a huge impact on disaster relief efforts, as studies revealed a variety of challenges, including a lack of sufficient SCM software and poor facilities. Despite its importance, few studies have explored the impact of disaster assistance on humanitarian organizations, particularly in Kenya. The study sought to address this gap by evaluating the impact of humanitarian logistics on emergency response in Nairobi, Kenya. Humanitarian groups, policymakers, and NGOs profited from the research, which informed strategic planning and policy creation to strengthen disaster response operations. The Resource Dependency Theory (RDT) proposed that organizations rely on external resources for survival and growth. It underlined the importance of forming strategic alliances to secure resources. In humanitarian settings, logistics were critical in procuring and allocating resources for successful emergency response. The research methodology used a cross-sectional descriptive design to investigate the association between humanitarian logistics management techniques and emergency response in Kenyan humanitarian organizations. Semi-structured questionnaires were used to obtain data from 64 Nairobi-based firms. Data analysis required the use of both descriptive and inferential statistical techniques, such as regression and correlation analysis. The study found a significant positive association (β = 0.732, p &lt; 0.001) between logistics management practices and emergency response, indicating that improving logistics leads to better response outcomes. The study recommended prioritizing logistical development and resource allocation to improve emergency response capabilities.

Workplace Buoyancy and Servant Leadership as Catalysts for Sustainable Disaster Management: Mitigating Emotional Exhaustion in Disaster Response Teams

Amid global crises like natural disasters and climate change, the emotional well-being of disaster response workers (DRWs) is a key factor in sustainable public health and disaster management. The study highlights the issue of emotional exhaustion among DRWs, which can impact organizational effectiveness and the health and well-being of affected communities. The study examines the roles of servant leadership (SL) and workplace buoyancy (WB) in addressing these issues. Using data from 336 DRWs involved in the 2023 Kahramanmaraş earthquake, JD-R Theory is employed to study how these factors interact in high-demand scenarios. Analysis with the Smart PLS 4 program reveals that SL and WB play key roles in reducing emotional exhaustion (EE). Notably, WB partially mediates the relationship between SL and EE. These insights are vital for creating sustainable public health and disaster management strategies in times of growing conflicts and climate crises. By extending the JD-R Theory to encompass these elements, the study provides valuable suggestions for policymakers and managers to enhance the resilience and well-being of DRWs. The study deepens our understanding of public health challenges in disaster settings and highlights the need for integrated, sustainable responses to support those on the front lines of disaster relief efforts.

Is there a case for gridshell structures in humanitarian assistance and disaster relief efforts?

This article focuses on the core concerns of shelter and settlement, and more specifically focuses on large-span shelters in humanitarian assistance and disaster relief (HA/DR) efforts. Furthermore, this article introduces the audience to novel large-span structures called gridshells. This paper presents a parametric, computational study that quantifies the efficacy of bracing orientation and arrangement on the load carrying capacity of gridshells. The varied parameters include the grid density (“16 by 16” to “30 by 30”) and the bracing scheme (unbraced, continuous fully-braced, discontinuous fully-braced, continuous half-braced, and discontinuous half-braced). Four load cases were analyzed, namely a symmetric distributed load, an asymmetric distributed load, a centered point load, and an off-centered point load. The results of the study provide designers with trends and basic design options upon which they can base further analysis depending on the requirements of the final structure.

Utilising crowdsourcing and text mining to enhance information extraction from social media: A case study in handling COVID-19 supply requests in Thailand

Social media platforms are critical for disaster communication and relief efforts. Rapid and precise social media post analysis is required for effective disaster response. This article presents a comprehensive study of a framework that combines crowdsourcing and text mining techniques to enhance data extraction from social media. The research focuses on a particular case study of COVID-19 pandemic medical supply request, which shows several key findings. First, the incorporation of domain-specific data during the training of named entity recognition (NER) models is essential for accurately identifying and retrieving important entities, such as the names of medical supplies and hospitals. Second, the implementation of a hybrid system leads to improvement in the extraction of information from social media posts. Finally, the involvement of crowdsourcing is found to be significant in the validation, verification, and filtering of disorganised information within the hybrid system. Our performance analysis demonstrates that the use of hybrid models has the potential to significantly improve the extraction of supply names (by up to 37%) and hospital names (by up to 66%), especially in the absence of a comprehensive vocabulary or specially trained NER models. During the COVID-19 supply shortage in Thailand, volunteers utilised hybrid models to expedite the identification of the necessary information. Experiment results demonstrated significant improvement in the accuracy of extracted data, the ability to acquire relevant information in real-time, the capacity to handle a substantial number of posts and the practical benefit of the proposed framework.

Towards decentralized resource management for disasters: NGO-RMSD

The necessity for an efficient resource management system during and postdisaster is underscored by the prevalent coordination and resource management challenges faced by various organizations, including non-governmental organizations (NGOs) and government units. To address these problems, our system proposal introduces NGO-RMSD, a blockchain-based decentralized system designed to enhance collaborative efforts during and after disasters. Utilizing the Quorum framework for its energy efficiency, NGO-RMSD enables seamless transactions and updates across parties, eliminating intermediaries and fostering a trusted environment for resource management. This paper details the development of this decentralized system, incorporating smart contracts for autonomous operation. Decentralized systems provide superior security, transparency, and immutability, making them fundamentally more robust and efficient than centralized systems for managing complex, multi-stakeholder scenarios like dis-aster response. This decentralized model ensures transparency, eliminates intermediaries, and enables all parties to conduct transactions and updates reliably. These contracts effectively assess and prioritize the needs of affected individuals, ensuring timely and accurate resource allocation. A proof of concept has been implemented, demonstrating the practicality and potential impact of NGO-RMSD. Designed in alignment with NGO's field requirements and distributed under a free software license, this system promises to significantly improve coordination for disaster resource management. Possible improvements and future work for a more sustainable system is discussed. Looking forward, we aim to enhance the efficiency and effectiveness of disaster relief efforts by providing assistance to a larger number of affected individuals in a more organized and expeditious manner.

Increasing portion of U.S. donors giving bulk of donations to disaster relief

A sizeable majority of U.S. donors gave at least half of their charitable contributions to disaster relief efforts last year, according to a new survey conducted by The Harris Poll on behalf of Vanguard Charitable, a leading administrator of donor‐advised funds.

Flood Detection in Polarimetric SAR Data Using Deformable Convolutional Vision Model

Floods represent a significant natural hazard with the potential to inflict substantial damage on human society. The swift and precise delineation of flood extents is of paramount importance for effectively supporting flood response and disaster relief efforts. In comparison to optical sensors, Synthetic Aperture Radar (SAR) sensor data acquisition exhibits superior capabilities, finding extensive application in flood detection research. Nonetheless, current methodologies exhibit limited accuracy in flood boundary detection, leading to elevated instances of both false positives and false negatives, particularly in the detection of smaller-scale features. In this study, we proposed an advanced flood detection method called FWSARNet, which leveraged a deformable convolutional visual model with Sentinel-1 SAR images as its primary data source. This model centered around deformable convolutions as its fundamental operation and took inspiration from the structural merits of the Vision Transformer. Through the introduction of a modest number of supplementary parameters, it significantly extended the effective receptive field, enabling the comprehensive capture of intricate local details and spatial fluctuations within flood boundaries. Moreover, our model employed a multi-level feature map fusion strategy that amalgamated feature information from diverse hierarchical levels. This enhancement substantially augmented the model’s capability to encompass various scales and boost its discriminative power. To validate the effectiveness of the proposed model, experiments were conducted using the ETCI2021 dataset. The results demonstrated that the Intersection over Union (IoU) and mean Intersection over Union (mIoU) metrics for flood detection achieved impressive values of 80.10% and 88.47%, respectively. These results surpassed the performance of state-of-the-art (SOTA) models. Notably, in comparison to the best results documented on the official ETCI2021 dataset competition website, our proposed model in this paper exhibited a remarkable 3.29% improvement in flood prediction IoU. The experimental outcomes underscore the capability of the FWSARNet method outlined in this paper for flood detection using Synthetic Aperture Radar (SAR) data. This method notably enhances the accuracy of flood detection, providing essential technical and data support for real-world flood monitoring, prevention, and response efforts.

An improved genetic algorithm with density-guiding operator based on the location selection model of emergency logistics center

In disaster relief efforts, establishing logistical channels with the affected population is crucial. This necessitates the construction of temporary logistics distribution centers. However, rapidly selecting suitable locations for these temporary logistics centers presents a considerable challenge. An inability to swiftly provide viable relief strategies can have significant negative implications for both the government’s reputation and the overall effectiveness of the relief efforts. In terms of problem modeling, the majority of researchers base their models on economic variables, with few considering the issue of panic among the disaster-affected populace. However, it is critical to address not only the widespread panic but also to focus on proactive rescue operations for those who are excessively panic-stricken or less mobile due to the disaster. In this article, we create an improved genetic algorithm with a density-guiding operator based on the location selection model of emergency logistics centers to address these issues. During variation iteration, the algorithm introduces population density as a guide and makes the algorithm’s parameters adaptive and dynamic, which can successfully choose a superior option. Finally, numerical tests are run on the Jiading District of Shanghai data set to confirm the algorithm’s efficacy. The final improvement of the adaptive algorithm is 13.21%.

Insights from China: understanding the impact of community resilience and government trust in psychological resilience and anxiety during COVID-19.

COVID-19 has the potential to greatly impact an individual mental well-being. However, an individual's psychological resilience, combined with support from their community and government disaster relief efforts can aid individuals in confronting crises with a positive mindset. The purpose of this study is to investigate how individuals, across three dimensions of individual resilience perception, community resilience perception, and government trust perception, mitigate individual anxiety during COVID-19. This study employed an online survey method that was not restricted by geographical location. Data collection took place from January 2022 to June 2022, and the valid questionnaires covered all 31 provinces, autonomous regions, and municipalities in China. The assessment of community resilience was conducted employing the Conjoint Community Resilience Assessment Measure-10 (CCRAM-10). Structural Equation Modeling (SEM) was also used to examine the relationship between community resilience, government trust, individual psychological resilience, and anxiety. The SEM results reveal that individual psychological resilience is significantly negatively correlated with anxiety (b = -0.099, p < 0.001), while there is a significant positive correlation between community resilience perception (b = 0.403, p < 0.001) and government trust (b = 0.364, p < 0.001) with individual psychological resilience. Furthermore, government trust perception enhances psychological resilience, consequently reducing anxiety (b = -0.036, p < 0.001). The results also revealed that women and increasing age had a mitigating effect on individual anxiety during COVID-19. Individual's mental state is influenced on multiple dimensions during COVID-19. Not only can individual psychological resilience better cope with anxiety, but support at the community and government dimensions has a significant impact on individual psychology. These resources can enhance the resilience of both individuals and communities, helping them better cope with stress and difficulties.