Disaster Events Research Articles

Facility Compliance and Natural Disaster Events: Evidence From the U.S. Clean Air Act

With the increasing severity and frequency of natural disasters, we investigate the impact of such events on company environmental performance in the United States. This study analyzes the effect of natural disasters on facility compliance with the Clean Air Act throughout the sample period from 2013 to 2022. Our findings show that facilities located in disaster areas are associated with higher violation rates. By examining individual types of disasters separately, we find that the results are most pronounced for floods, fires, and severe storms. We suggest that regulators collectively evaluate the environmental policies and practices for companies in areas prone to natural disasters.

Open-world disaster information identification from multimodal social media

The application of multimodal deep learning for emergency response and recovery, specifically in disaster social media analysis, is of utmost importance. It is worth noting that in real-world scenarios, sudden disaster events may differ from the training data, which may require the multimodal network to predict them as unknown classes instead of misclassifying them to known ones. Previous studies have primarily focused on model accuracy in a closed environment and may not be able to directly detect unknown classes. Thus, we propose a novel multimodal model for categorizing social media related to disasters in an open-world environment. Our methodology entails utilizing pre-trained unimodal models as encoders for each modality and performing information fusion with a cross-attention module to obtain the joint representation. For open-world detection, we use a multitask classifier that encompasses both a closed-world and an open-world classifier. The closed-world classifier is trained on the original data to classify known classes, whereas the open-world classifier is used to determine whether the input belongs to a known class. Furthermore, we propose a sample generation strategy that models the distribution of unknown samples using known data, which allows the open-world classifier to identify unknown samples. Our experiments were conducted on two public datasets, namely CrisisMMD and MHII. According to the experimental results, the proposed method outperforms other baselines and approaches in crisis information classification.

A blue–green ratio of urban wetlands as an ecosystem health indicator: the case of urban sprawl in Nagpur, India

ABSTRACT Rampant urban sprawl has affected the structure and function of existing urban green and blue spaces, exposing developing cities to unexpected disaster events. Drawing on a mixture of secondary information supported by GIS and field-based studies, we assessed the impact of growing impervious surfaces on the dynamic zones of urban wetlands in Nagpur, India. We compared decadal data (2000-2020) to understand the pattern of wetland loss and its drivers in the region. Our findings reveal a significant loss of wetland structure and area over the past two decades, raising alarming concerns regarding ecological and economic repercussions. To address these issues, we propose a blue–green buffer-based assessment approach that incorporates local knowledge and observations along with proactive monitoring of ecosystem health. Our study highlights the urgency of reducing the spatial complexity of landscape patches, restoring wetland buffers, declaring wetland buffers as no-go zones, and promoting corridor connectivity to urban wetlands to improve urban blue infrastructure health. Multi-sectoral and stakeholder cooperation, involvement, and strict enforcement of the Wetland Rules (2017) by local governing bodies can help build urban resilience by protecting urban blue–green infrastructure.

Who Is Perceived as Deserving? How Social Identities Shape Attitudes about Disaster Assistance in the United States

ABSTRACT Research has shown that as the size of government assistance programs grow, and the recipients of such programs are increasingly non-white and/or non-citizen, public support for them declines. Our study examines this phenomenon on the question of deservingness in federal disaster assistance. Using a 2018 survey experiment that leverages two devastating hurricanes—Hurricane Maria and Hurricane Harvey—that hit different parts of the United States in 2017, we explore how the social identities of race/ethnicity and partisanship affect attitudes about disaster deservingness. Our results demonstrate that although federal disaster assistance has broad support, it is contingent on perceptions about the disaster victim and the type of assistance. Respondents were less likely to support disaster assistance to Hurricane Maria–affected people than those affected by Hurricane Harvey. Moreover, white and Republican respondents were more likely to favor market-based assistance whereas race-/ethnic-minority and Democratic respondents were more likely to support more generous forms of disaster assistance. These findings have important implications for the allocation of disaster funds as climate change intensifies and the frequency of billion-dollar disaster events increases. This is exacerbated by political polarization and heightened social vulnerability due to changing population demographics.

Performance Evaluation of Flexible Photovoltaic Panels for Energy Supply in Post-Disaster Emergency Shelters

In the event of disasters, the provision of energy encounters significant challenges. Flexible photovoltaic (FlexPV) is an ideal choice for emergency shelters to supply the daily activities for displaced populations. S(P)EEDKITS is a project to develop rapid deployable emergency shelters as seeds of self-recovery. Based on the project, this study is to assess the feasibility of FlexPV on the roof of emergency shelters. Various measurements have been conducted on a multipurpose tent to investigate the reliability of the flexible solar cells, thermal performance, acoustic performance, and power generation in the winter solstice. From the results, the Electroluminescence tests show that serious microcracks occur on the FlexPV cells after a long-term application, which is hidden danger for power output. I-V tests show the degradation of PCE is within 2.8% after one year usage Moreover, the energy yield during the winter solstice is sufficient to fulfill the minimum power requirement essential for the effective operation of the emergency shelter in Milan. Additionally, the integration of FlexPV panels causes an average 8.6 °C temperature increase, showing a negative influence on the power output of FlexPV. Finally, the integration leads to a 6.7% increase of the indoor noise. So, the FlexPV panels are more suitable to be applied for a short period of time after disaster to solve the energy crisis of disaster victims.

Digital transformation in public-private collaborations: The success of humanitarian supply chain operations

Recent years have seen the extensive use of big data analytics, related technological infrastructure, and machine learning applications for digital transformation. The resource dependency related to data-driven applications elicits public-private collaborations (PPCs) between governments and private or non-government organizations (NGOs) for value creation. Such collaborations are effective for the success of humanitarian supply chain operations (HSCOs), particularly in the event of large-scale disasters. By building on resource dependence theory (RDT), our study explores the links between digital transformation, PPCs, and HSCO success. Using structural equation modeling on data collected from 224 key decision-makers and experts, we found that digital transformation mediates the relationship between private-NGO collaborations and HSCO success while host government support moderates it. Our study thus makes an original contribution to RDT and the emerging domains of contemporary digital and data-driven applications in HSCO. The implications and future research directions arising from this study are also discussed in this research paper.

A Semantic Augmented Approach to FEMA P-58 based Dynamic Regional Seismic Loss Estimation Application

Regional seismic loss estimation (RSLE) is a crucial process in both immediate post-earthquake emergency response and long-term reconstruction endeavors. Over the years, significant progress has been made in RSLE: sensing approaches such as field investigation and remote sensing offers a comprehensive overview necessary for real-time disaster response, while simulation techniques such as the methodology proposed in Federal Emergency Management Agency (FEMA) P-58 series provide insights into the mechanism of disaster development and its potential long-term impacts on urban assets. Nonetheless, challenges persist in the realm of practical RSLE applications. Firstly, a dynamic understanding of a disaster event and its influence is deemed important for effective emergency responses while challenging to achieve in current approaches. Secondly, stakeholders with varying roles, including administrators, rescue teams and ordinary citizens have distinct information requirements for RSLE. Thirdly, the complexity of seismic loss estimation, involving diverse data sources such as building information models, performance models, fragility data and sensor observations, poses interoperability issue. To tackle these issues, this article introduces a dynamic, multi-granularity, ontological representation scheme tailored for RSLE decision-supporting systems. This scheme operates across various scales, from individual building components to broader regional scales by synergistically employing FEMA P-58 guidelines and Semantic Web technologies. Upon the corresponding semantics, a question-and-answer agent powered by large language model is further developed to facilitate interaction requirements within the RSLE process via FEMA P-58 pipeline. The practical efficacy of this approach is validated through a prototype deployed under a real earthquake event, demonstrating its value in real-world scenarios.

Real-time modeling of transient crustal deformation through the quantification of uncertainty deduced from GNSS data

We propose a new method for real-time uncertainty monitoring of earthquake and volcano source models using data from the global navigation satellite system (GNSS) and explore its application concerning observation station placement. The Geospatial Information Authority of Japan operates two main types of GNSS earth observation network system (GEONET) coordinates for crustal deformation monitoring on different time scales: post-processing analysis values and real-time GEONET analysis system for rapid deformation monitoring (REGARD). REGARD uses the Markov Chain Monte Carlo (MCMC) method termed real-time automatic uncertainty estimation of a coseismic single rectangular model using GNSS data (RUNE) for single rectangular fault model estimation to handle uncertainty. Thus far, no GNSS monitoring system can automatically detect transient crustal deformation events, such as volcanic activity and earthquake swarms, on timescales of a day or less. We extended RUNE and developed a core program for a new monitoring system for earthquake and volcanic source models and their uncertainties. Our program achieved automatic and stable MCMC utilization for rectangular fault, dike, Mogi, and spheroid models by increasing the computational speed, improving search efficiency, and adjusting hyperparameters. The program automatically determines the standard deviation of the likelihood function assuming a normal distribution with weights for each observation station. The calculation time was within 15 s for 1 × 106 samples on a standard 1U server. We assessed the reliability of the developed method using synthetic and observed GNSS data from the 2015 Sakurajima volcanic event. The results were consistent with the assumed model and previous studies and indicated an advantage in automatically quantifying uncertainty in a short computation time. Based on MCMC samples, we developed a new visualization algorithm to indicate areas on a map in which the number of observation stations should be expanded. We assessed the reliability using data from the 2023 Noto Peninsula earthquake [Mj 6.5]. The results indicate that the algorithm is helpful in studying the placement of stations. The above model extensions and their application are essential to achieve a rapid quantitative understanding of disaster events near urban areas and for utilizing this information in emergency response activities.Graphical

Modeling Emergency Traffic Using a Continuous-Time Markov Chain

This paper aims to propose a novel call for help traffic (SOS) and study its impact over Machine-to-Machine (M2M) and Human-to-Human (H2H) traffic in Internet of Things environments, specifically during disaster events. During such events (e.g., the spread COVID-19), SOS traffic, with its predicted exponential increase, will significantly influence all mobile networks. SOS traffic tends to cause many congestion overload problems that significantly affect the performance of M2M and H2H traffic. In our project, we developed a new Continuous-Time Markov Chain (CTMC) model to analyze and measure radio access performance in terms of massive SOS traffic that influences M2M and H2H traffic. Afterwards, we validate the proposed CTMC model through extensive Monte Carlo simulations. By analyzing the traffic during an emergency case, we can spot a huge impact over the three traffic types of M2M, H2H and SOS traffic. To solve the congestion problems while keeping the SOS traffic without any influence, we propose to grant the SOS traffic the highest priority over the M2M and H2H traffic. However, by implementing this solution in different proposed scenarios, the system becomes able to serve all SOS requests, while only 20% of M2M and H2H traffic could be served in the worst-case scenario. Consequently, we can alleviate the expected shortage of SOS requests during critical events, which might save many humans and rescue them from being isolated.

Forest History Association of Wisconsin

The Enterprise Radiation Forest (ERF) study was initially introduced as a topic of discussion at the Institute of Forest Genetics, USDA, USFS, in the late 1960s. During that period, a program was endorsed for investigating the impacts of radiation on the forest ecosystem. The rationale for this was to facilitate the prediction of potential outcomes in the occurrence of a nuclear disaster. The research location was situated within the forested lands in Enterprise of Oneida County, Wisconsin, United States. The radiation source in question was a 10,000 curie source of Cesium-137, characterized by a halflife of 33 years. Furthermore, a study concerning lichens was also carried out, the data, photographs, and micrographs were meticulously gathered to illustrate the pre-irradiation activity of the species. This subject holds significant value for dissemination among peers in the scientific community, particularly considering the resilience exhibited by natural ecosystems in response to varying levels of radiation. The information is reviewed with the objective of the entire program to ascertain the potential consequences for a northern forest ecosystem in the event of a nuclear disaster, in addition to the two publications from the Atomic Energy Commission.

Aboriginal flood narratives and the thunder complex in Southeast Asia

AbstractThe ancestors of the dingo were brought to mainland Australia around 4000 years ago by people who arrived by boat. The identity of these voyagers from the north, however, and the nature of their interactions with the Aboriginal population of Australia, are unknown. Here, we propose that Indigenous flood narratives from the Kimberley contain evidence for contact between Aboriginal people and early Asian seafarers in the form of the “thunder complex”. The latter is a very specific repertoire of taboos, rituals and stories that occurs widely among ethnographically known societies of Indonesia, the Philippines and peninsular Malaysia, but has not previously been identified in Australia. Among Southeast Asian groups, this cultural complex revolves around the idea that certain prohibited acts perpetrated against animals – especially “mocking” them by treating them as though they were human – precipitate a punitive storm and/or flooding. We show that in some oral traditions of the Kimberley region animal mockery is similarly held to be the causative agent behind disastrous flooding events that took place in the past creationary epoch. We contend that this localised Aboriginal variant of the thunder complex reflects an episode of close interaction with early Austronesian‐speaking voyagers who introduced ancestral dingoes to mainland Australia, apparently via the Kimberley coast.

THE INSTITUTIONAL COMPETENCIES OF THE STATE PUBLIC SAFETY AGENCIES OF GOIÁS AND THEIR PROPENSION TO RESPOND TO DISASTERS

This article seeks, based on a review of the institutional competencies of state public safety agencies, to relate these competencies to the propensity of each agency to be activated in the event of disasters. A survey of the legislation was carried out, especially of the public safety agencies of the State of Goiás, outlining their competencies from the provisions of the Federal Constitution, in addition to a brief bibliographic review on disaster response. In the treatment of the four state public safety institutions provided for in the Brazilian Constitution, it was evident that, due to its institutional competencies, the Military Fire Department is the agency most likely to be activated in response to disasters. Thus, it is possible to take a look at the institutions, including in the sense of formulating organizational policies aimed at providing care to professionals.

Association Between Social Determinants of Health, COVID-19 Stressors, and Mental Health Among New York Residents Early in the Pandemic.

The COVID-19 pandemic is a disaster event. Exposure to stressors during and after disaster events is associated with negative mental health symptoms. To inform targeted COVID-19 recovery efforts, data are needed to understand which stressors play a key role in this relationship. Cross-sectional survey data (demographics, impacts of COVID-19, social determinants of health, depression, and anxiety) were collected online from adults living in New York state between May and June 2020. Differences in the proportion of stressors (COVID-19 and social determinants) experienced by race/ethnicity were assessed using chi-square analyses. Logistic regression was used to assess which factors were associated with increased odds of depression and anxiety. A majority (n = 258, 62.2%) of the 415 respondents reported being directly impacted by the pandemic. Non-white respondents reported a significantly larger proportion of stressors compared to white respondents. Under half of respondents reported depression (n = 171, 41.2%) and anxiety (n = 164, 39.5%). Healthcare and food concerns were associated with increased odds of depression and anxiety, and economic concerns were associated with increased odds of anxiety. Findings underscore the need to respond to the COVID-19 mental health crisis by addressing social determinants of health.

Leveraging Social Media Data for Forest Fires Sentiment Classification: A Data-Driven Method

Background: The rise in forest fires over the last two years, which is due to rise in dry weather conditions and human activities, have greatly impacted an area of 1.6 million hectares, leading to significant ecological, economic, and health issues, hence the need to improve disaster response strategies. Previous research determined the lack of coverage regarding public response during forest fires with conventional methods such as satellite images and sensor data. However, social media platforms provide real-time information generated by users, along with location information of disaster events. Sentiment analysis helps in understanding the public reactions and responses to natural disasters, thereby increasing awareness about forest fires. Objective: The purpose of this research is to assess the efficiency of Long Short-Term Memory (LSTM) method in classifying sentiment for social networks in regard to forest fires. This research aims to examine the effect of TF-IDF, unigram, and the FastText features on the effectiveness of the classification of sentiment. Methods: The precision, recall, and F1 score of 2, 3, and 4 determined in the LSTM models with commonly available sentiment analysis tools, such as the Vader Sentiment Analysis and SentiWordNet was used to evaluate the performance of the model. Results: With an improvement of roughly 10%, the four layers of the LSTM model generated the best performance for the evaluation of sentiments about forest fires. The LSTM model with FastText achieved F1, recall and precision scores of 0.649, 0.641, and 0.659, which exceeds lexicon-based method including SentiWordNet and Vader. Conclusion: The experimental results showed that the LSTM model outperformed lexicon-based methods when used to analyse the tweets related to forest fire. Additional research is required to integrate rule-based models and LSTM models to develop a more robust model for dynamic data. Keywords: Forest Fire, Disaster, Long Short-Term Memory, LSTM, Vader, SentiWordnet

Exploring the use of satellite Earth observation active wildland fire hotspot data via open access web platforms

ABSTRACT Globally, managing wildland fire is increasing in complexity. Satellite Earth Observation (EO) data, specifically active fire ‘hotspot’ data, is often used to inform wildland fire management. This study explores hotspot data usage via web traffic data (‘user counts’) for the FIRMS, GWIS and EFFIS web portals between September 2019 and April 2023. Global active fire data use is characterized by multi-month periods of relatively low, stable user counts, interspersed with periodic spikes (4.1x median monthly activity) of activity broadly aligned with the North American / European fire season (late summer-fall). Users from the Americas (45%) and Europe (36%) dominate web traffic. We also examined correlative relationships between web page user counts and environmental and social variables at multiple spatial scales. Globally, the strongest relationships were found between user counts and the total number of fires, total burned area, number of fire disaster events; country population levels and internet proliferation. Notably, at a country level, the strongest (and weakest) relationships between weekly user counts and fire activity were found in a diverse range of countries suggesting that a variety of hotspot data use situations exist and further investigation is needed to better understand EO active fire data usage.

New Methodology for Assessing Seismic Ground Vulnerability Based on Microtremor and Taiwan Seismic Intensity Scale

ABSTRACT The Taiwan Ground Deformation Index (TGDI) is proposed to quantify site vulnerability using a non-linear soil deformation model, PGA parameters from the Taiwan seismic intensity scale, and microtremor analysis. Site vulnerability is graded into four levels: low (TGDI < 2.4), moderate (2.4 ≤ TGDI < 4.2), high (4.2 ≤ TGDI < 7.4), and very high (TGDI ≥ 7.4), based on susceptibility to seismic damage. Validating TGDI with 13 disaster events in Tainan during the 2016 Meinong earthquake showed 12 cases of moderate-to-high vulnerability. Since weak ground and strong amplification properties favor TGDI increases, this index serves as an early warning parameter for disasters.

A Comprehensive Review of Remote Sensing Technologies for Improved Geological Disaster Management

ABSTRACTOver the past two decades, the frequency of natural hazard incidents has steadily risen, leading to substantial human casualties, infrastructure destruction, societal and economic disruption. The occurrence of disasters, both of natural and human origin, has exhibited an upward trend in frequency over the past few decades on a global scale, posing a significant threat to diverse populations. Over time, remote sensing technologies have proven to be effective in analysing and monitoring diverse natural disasters, including but not limited to droughts, earthquakes, tsunamis, landslides and cyclones. The significant extent of its coverage capacity and the ability to repeat observations make its application economically efficient. This paper aims to elucidate the fundamental contributions and role of remote sensing in disaster management applications. In a comprehensive analysis, this study explores recent practical applications in the context of disaster events. The utilisation of diverse methodologies and functions of remote sensing in disaster monitoring and control is further expanded to encompass the domain of disaster risk management, employing cutting‐edge sensors and satellites from emerging technological advancements. This paper also addresses challenges related to disaster monitoring, detection and management. Emergencies, particularly during typical catastrophe scenarios, often witness partial disruptions in communication networks. Consequently, the role of alternative networks becomes paramount in enabling effective disaster detection and management strategies. Emerging issues are highlighted, and potential directions for future research are outlined and also support sustainable development goal 13 (climate action).

Near Real-Time Responsive Flood Event Representation: An Open-Source Interactive Web Application Architecture

Abstract. The adoption of accessible Digital Twin technology in flood applications has been impeded by a notable lack of practical examples. Arguably, the most distinguishing feature of Digital Twin technology is the integration of near real-time data analytics through IoT sensor connectivity, which has remained underutilised in flood applications and therefore indicates a critical research gap. This project endeavoured to develop a comprehensive and replicable open-source architecture framework, specifically tailored for near real-time responsive flood event representation. The resultant interactive web application integrated near real-time river height and rain fall data streams and performed on-demand data analytics. Key results include the establishment of a back-end spatial database, a coupled physical city and digital space model, and a functional holistic front-end user interface. Additionally, this research aligned with the Gemini Principles by emphasising data interoperability and maintaining an information feedback loop. The study also aimed to configure near real-time IoT sensor connections, implement event triggers, and deliver interactive visualisations in an easily accessible format. The research addressed key questions surrounding the effective integration of IoT sensor data, identification of crucial flood indicators and parameters, and rapid quantification of flood event impacts. Ultimately, this research project demonstrates how Digital Twin technology can swiftly provide decision-makers with crucial insights during flood disaster events.

Spatial evaluation rainfall estimation on weather radar using Marshall–Palmer reflectivity–rainfall rate in Banten

Based on data from the National Disaster Management Agency (Ind.: Badan Nasional Penanggulangan Bencana – BNPB), throughout 2022, more than 91% of disaster events were hydrometeorological disasters, with floods at 43% and landslides at 17%. One of the factors for floods and landslides is high rainfall intensity. Automatic rain gauge (ARG) is a rainfall observation instrument that can accurately measure rainfall at observation points. However, it has problems such as communication systems that cause delays in data transmission, low instrument density, and inability to cover a wide spatial area, which can affect the accuracy of rainfall information. Weather radar is a remote sensing instrument that can estimate rainfall spatially so that weather radar observations can reach areas of the region that do not have ARG. However, before being used as rainfall information, estimation rainfall needs to be evaluated or calibrated. Evaluation of rainfall estimation on weather radar to ARG in Banten at a 30– 120 km distance range, shows a coefficient of determination above 0.8. Based on the studies that have been conducted, increase of root mean square error (RMSE) is due to influence of radar observation range and observation distance on ARG. Adjustment of rainfall estimation improves the accuracy of rainfall estimation. Adjusting rainfall estimation can reduce RMSE by 50%.

Research on Emotion about Media Memory of Wenxi Fire in Central Daily News

The author conducted a comprehensive study on the emotions reflected in The Central Daily News coverage of the Wenxi Fire, utilizing the digital analysis tool ROST CM6 in combination with historical research. The media memory of the Wenxi Fire in The Central Daily News reflects the emotional response and guidance of the National Government towards this disastrous event. The newspaper's spirited stance on the War of Resistance against Japan invigorated the public’s morale. Through selective reporting and, at times, the outright omission of facts, the newspaper initially mitigated the public’s criticism towards the central and Hunan provincial governments during the first phase, when the cause of the fire was still under investigation. In the second phase, after the cause was determined, the newspaper successfully shifted the blame for this governmental failure to the Japanese invaders, the Hunan provincial government, and the “righteously indignant” public. Through a series of carefully orchestrated actions, the newspaper managed to quell public anger, boost morale, and foster a stronger sense of national unity under the National Government, ultimately enhancing its image.