Disaster Events Research Articles

Science Environment Technology And Society Based For Disaster Mitigation

In facing the large number of disasters in Indonesia, efforts to reduce disaster risk have become a strong foundation for joint efforts. One of the disaster mitigation learning methods developed is the Science Environment Technology and Society (SETS) learning model. Therefore, this research aims to develop natural disaster learning tools with a SETS vision that are integrated in science subjects, implement disaster teaching materials with an integrated Science Environment Technology and Society vision in science subjects, increase the understanding and skills of teachers and students regarding concepts, principles and practicing self-rescue in the event of a natural disaster, and increasing collegiality between lecturers and teachers as well as between teachers in teaching disaster material to students. This multi-year development research (R&D) was carried out in collaboration with teachers in primary and secondary education. The research results are in the form of five learning model features such as: Syllabus, lesson plans, learning methods, teaching materials, as well as techniques and types of assessment developed including science material for grades IV, V, VI primary school and VII, VII, IX secondary school. The five features are packaged in a Guidebook for Teaching Natural Disasters Integrated in Science (for Teachers), a Textbook (for Students) and a supplement in the form of a cartoon comic. The dissemination results show that the learning tools developed are worthy of being given to students, and can increase students' understanding in recognizing and dealing with disaster.

Exploring the most effective feedback system for training people in Earthquake emergency preparedness using immersive virtual reality serious games

Natural hazards, such as earthquakes, can cause enormous financial and human fatalities, particularly in congested public areas, such as shopping malls. To reduce the risk of injury or death in such situations, it is vital that people behave correctly and respond quickly. Due to the shortcomings and incapabilities of traditional training methods to simulate emergency situations, technology has provided opportunities to simulate real-life emergency scenarios, so that more effective training can be provided for users of public areas during disastrous events. As such, Immersive Virtual Reality (IVR) and Serious Games (SGs) are used in this study to investigate five methods in training system required for emergency conditions to rescue people. These methods include text-based immediate feedback, visual-based immediate feedback, audio-based immediate feedback, post-game assessment, and spiral feedback. An extensive experiment involving 145 trainees was conducted based on two approaches: 1) safety knowledge, and 2) self-efficacy. According to the findings, the IVR SG training system with the spiral feedback method was more effective in acquiring knowledge than visual- and audio-based immediate feedback. However, there was no significant difference between groups' performance in improving self-efficacy. In addition, the research investigated the impact of five different methods on improving self-efficacy statements individually, yielding intriguing results. Finally, the findings of this study provide significant practical implications for safety and emergency awareness for users, and preparedness for facility managers to provide better training during emergencies.

Feasibility of anomalous event detection based on Mobile Spatial Statistics: A study of six cases in Japan

One of the primary goals of disaster risk management is to minimize the loss of life in the event of a disaster. To reduce the number of victims in disasters is important to know the amount of exposed population to a hazard. Similarly, population distribution in real-time is required to estimate post-disaster needs and resources for disaster relief. In this study, we explore the use of Mobile Spatial Statistics (MSS) as the basis to acquire offline and online population data and detect anomalies that may relate to a disaster. For instance, mobile spatial data could identify sudden changes in the number of people indicating an anomalous event or change in population dynamics. Here we use the Matrix Profile (MP) method in time-series analysis to test the feasibility of detecting anomalies in the MSS data. We selected mass gatherings and disaster events with rapid and slow onset and small and large scales. We discuss the applicability of this method for real-time anomaly detection of spatial population distribution within the context of disaster risk management. Our results show that the applicability of the MP method in anomaly detection is sensitive to the size of the areas used during monitoring and analysis, the subsequence length and the threshold applied.

Optimizing Disaster Response through Efficient Path Planning of Mobile Aerial Base Station with Genetic Algorithm

The use of unmanned aerial vehicles (UAVs), or drones, as mobile aerial base stations (MABSs) in Disaster Response Networks (DRNs) has gained significant interest in addressing coverage gaps of user equipment (UE) and establishing ubiquitous connectivity. In the event of natural disasters, the traditional base station is often destroyed, leading to significant challenges for UEs in establishing communication with emergency services. This study explores the deployment of MABS to provide network service to terrestrial users in a geographical area after a disaster. The UEs are organized into clusters at safe locations or evacuation shelters as part of the communication infrastructure. The main goal is to provide regular wireless communication for geographically dispersed users using Long-Term Evolution (LTE) technology. The MABS traveling at an average speed of 50 km/h visits different cluster centroids determined by the Affinity Propagation Clustering (APC) algorithm. A combination of graph theory and a Genetic Algorithm (GA) was used through mutators with a fitness function to obtain the most efficient flyable paths through an evolution pool of 100 generations. The efficiency of the proposed algorithm was compared with the benchmark fitness function and analyzed using the number of serviced UE performance indicators. System-level simulations were used to evaluate the performance of the proposed new fitness function in terms of the UEs served by the MABS after the MABS deployment, fitness score, service ratio, and path smoothness ratio. The results show that the proposed fitness function improved the overall service of UEs after MABS deployment and the fitness score, service ratio, and path smoothness ratio under a given number of MABS.

Evaluation of community engagement for resilience outcomes: A pre-engagement approach

Recent disasters have placed enormous pressure on communities to be more resilient. Community resilience encompasses a range of structural, economic, and social dimensions that affect a community's ability to withstand change and disruption from disaster events. In recognition of the social processes that contribute to resilience, governments have increased investment in community engagement programs based on the assumption these programs of activity contribute to community resilience. While several studies identify the indicators of community resilience, few operationalize these indicators for community engagement in a way that can be used to establish baselines and evaluate change overtime. This paper addresses this gap. In-depth interviews with 16 community engagement practitioners in Australia investigated how community resilience in flood prone communities was conceptualized and how the outcomes from engaging communities for resilience building were identified. Findings from the study and the extant literature on resilience empirically informed the development of a four-step pre-engagement approach that extends current community engagement models and supports the measurement of engagement activities and aligned resilient outcomes. The pre-engagement approach can be adapted to diverse community contexts beyond flood emergencies and beyond Australian borders.

Temporal patterns and life cycle dynamics of social media user activity during disasters: A data-driven approach for effective crisis communication

Situational awareness of social media user activity during disasters is essential for effective and efficient crisis communication. Social media platforms such as Twitter and Facebook have emerged as critical channels for governments, relief agencies, and volunteer organizations to disseminate important information to the public during disasters. However, existing models are often limited to a specific disaster event, lacking a comprehensive and generalized approach. This paper introduces a novel life cycle concept to investigate the dynamics of social media engagement during multiple wildfires and bombing/shooting events in the United States. We extensively analyze millions of tweets and identify significant temporal commonalities, unveiling generic diurnal and life cycle patterns within each disaster category. This study proposes two models for the Hourly Distribution of Tweets and Daily Distribution of Tweets to explore different evolutionary stages characterizing diurnal and life cycle patterns. The hourly model accurately calculates peak engagement times for wildfire and bombing/shooting events, with peak engagement occurring between 9:00 AM and 12:00 PM. The daily models reveal that the highest diffusion rate is observed between two to four days from the wildfire formation day, while the peak engagement of a bombing/shooting event comes within the first 24 h of the incident. The findings enhance the understanding of social media dynamics during disasters and assist crisis communication agencies in developing communication strategies that ensure prompt and effective dissemination of critical information to affected populations.

Short- and long-term destination resilience in Wuhan, China under COVID-19: Differences in scenic spot type and origin market structure

Analyzing changes in destination resilience can inform critical but under-explored linkages characterizing disaster events and recovery. Based on web search data from 2011 to 2021, this study applied Tourism Background Trend Line (TBTL) modeling to calculate tourism resilience of 41 scenic spots in Wuhan, China under COVID-19. Existing government classifications and market demand data informed scenic spot categorization into three groups based on attraction type (cultural, leisure, natural) or domestic origin market structure (high, moderate and low dependence on the local market). Two-way ANOVAs tested differences in short- and long-term resilience among these different groups. Results indicate that scenic spots with low dependence on the local market were most resilient in the short-term (Jan–April 2020), while scenic spots with high dependence on the local market and natural scenic spots were most resilient in the long-term (May 2020–Dec 2021). Findings highlight heterogeneities in destination resilience at the micro-scale within a single destination, encouraging managers to develop targeted marketing strategies for various scenic spots at different stages of crises like COVID-19. The study's novel framework and implications also inform short-versus long-term resilience theory while accentuating destination recovery strategies in post-pandemic contexts.

A unified framework for evaluating the resilience of critical infrastructure: Delphi survey approach

This study advocates establishing an indicator system for Critical Infrastructure (CI) resilience assessment to ensure consistency and comparability in future endeavors. Resilience has emerged as a fundamental framework for effectively managing the performance of CIs in response to the challenges posed by disaster events. However, it is evident that a lack of uniformity exists in the choice and standardization of resilience assessment across the identified frameworks. This paper proposes key attributes for facilitating resilience assessment of CIs using an in-depth literature survey for identification and two rounds of Delphi survey in the Sri Lankan context for their verification. The literature survey has analyzed the resilience assessment attributes under four types of capacities: planning (anticipative), absorptive, restorative, and adaptive. Twenty-seven resilience attributes (Planning: 6; Absorptive: 12; Restorative: 6; Adaptive: 3) under different capacities were identified, including sub-indicators for evaluating each resilience attribute. Outcomes of the Delphi survey were analyzed through descriptive statistics. The proposed attributes received high levels of agreement from the experts, indicating their suitability and applicability for assessing the resilience of the CIs. The mean ratings of the attributes varied from 4.0 to 5.0, with the majority exceeding 4.5 out of 5. The evaluation of these attributes will be useful for assessing the resilience capacity of the CIs and thereby to model the overall resilience of the CIs. The results of this study will provide a solid basis for formulating hypotheses in future research aimed at assessing CI resilience.

Analysis of emergency assembly points for post-earthquake disaster management: a case study of Erzincan, Türkiye

The selection of emergency assembly points within the urban area holds significant importance for ensuring swift and effective intervention in the aftermath of a potential disaster until temporary shelter areas are prepared. In both historical and instrumental periods, Erzincan (Türkiye) has experienced major earthquakes resulting in significant loss of life and property. The likelihood of similar earthquakes occurring in Erzincan in the future remains high. Therefore, it is necessary to identify safe areas where people can assemble after an earthquake, moving away from hazardous zones. The aim of this study is to analyse the capacities and adequacies of emergency assembly points, which constitute a step in disaster management and enhance the earthquake resilience of the city, using Geographic Information Systems (GIS) based on selected criteria (accessibility, spatial distribution, proximity to healthcare facilities, size, capacity adequacy, and proximity to fault avoidance zones), and to propose solutions. The assessment revealed that the available assembly points are not homogeneously distributed throughout the city and are inadequate to meet the available building and population density. Therefore, in addition to the available 38 emergency assembly points, 67 new alternative emergency assembly points have been identified. The proposed emergency assembly points (in the 75–100% range) increased the number of neighborhoods within the 250 m service area by 43.75% and 42.5% for 500 m.The findings underscore the necessity for more comprehensive and effective planning for Erzincan in the event of a potential disaster or emergency.

Facial recognition for disaster victim identification

Mass disaster events can result in high levels of casualties that need to be identified. Whilst disaster victim identification (DVI) relies on primary identifiers of DNA, fingerprints, and dental, these require ante-mortem data that may not exist or be easily obtainable. Facial recognition technology may be able to assist. Automated facial recognition has advanced considerably and access to ante-mortem facial images are readily available. Facial recognition could therefore be used to expedite the DVI process by narrowing down leads before primary identifiers are made available. This research explores the feasibility of using automated facial recognition technology to support DVI. We evaluated the performance of a commercial-off-the-self facial recognition algorithm on post-mortem images (representing images taken after a mass disaster) against ante-mortem images (representing a database that may exist within agencies who hold face databases for identity documents (such as passports or driver's licenses). We explored facial recognition performance for different operational scenarios, with different levels of face image quality, and by cause of death. Our research is the largest facial recognition evaluation of post-mortem and ante-mortem images to date. We demonstrated that facial recognition technology would be valuable for DVI and that the performance varies by image quality and cause of death. We provide recommendations for future research.

Xaminer: An Internet Cross-Layer Resilience Analysis Tool

A resilient Internet infrastructure is critical in our highly interconnected society. However, the Internet faces several vulnerabilities, ranging from natural disasters to human activities, that can impact the physical layer and, in turn, the higher network layers, such as IP links. In this paper, we introduce Xaminer, the first Internet cross-layer resilience analysis tool, to evaluate the interplay between physical-and network-layer failures. Using a cross-layer Internet map and a failure event model, Xaminer generates a risk profile encompassing a cross-layer impact report, critical infrastructure identification at each layer, and the discovery of trends and patterns under different failure event settings. Xaminer's key strengths lie in its adaptability to diverse disaster scenarios, the ability to assess risks at various granularities, and the capability to generate joint risk profiles for multiple events. We demonstrate Xaminer's capabilities in cross-layer analysis across a spectrum of disaster event models and regions, showcasing its potential role in facilitating well-informed decision-making for resilience planning and deployments. The Xaminer tool is available as open-source software.

GoFundMe.org launches new initiative to help communities prepare for disaster events

GoFundMe.org launches new initiative to help communities prepare for disaster events

Multi-resolution monitoring of the 2023 maui wildfires, implications and needs for satellite-based wildfire disaster monitoring

The August 2023 wildfires over the island of Maui, Hawaii were one of the deadliest U.S. wildfire incidents on record with 100 deaths and an estimated U.S. $5.5 billion cost. This study documents the incidence, extent, and characteristics of the 2023 Maui wildfires using multi-resolution global satellite fire products, and in so doing demonstrates their utility and limitations for detailed fire monitoring, and highlights outstanding satellite fire observation needs for wildfire monitoring. The NASA 500 m Moderate Resolution Imaging Spectroradiometer (MODIS) burned area product is compared with PlanetScope 3 m burned areas that were mapped using a published deep learning algorithm. In addition, all the August 2023 active fire detections provided by MODIS on the Terra and Aqua satellites and by the Visible Infrared Imaging Radiometer Suite (VIIRS) on the S-NPP and NOAA-20 satellites are used to investigate the geographic and temporal occurrence of the fires and their incidence relative to the 3 m mapped burned areas. The geographic and diurnal variation on the fire radiative power (FRP), available with the active fire detections, is presented to examine how energetically the fires were burning. The analysis is undertaken for all of Maui and for the town of Lahaina that was the major population center that burned. Satellite active fires were first detected August 8th, 2023 in the early morning (1:45 onwards) on the western slopes of Mt. Haleakalā and were last detected August 10th in the early morning (at 2:46) over Lahaina and on the western slopes of Mt. Haleakalā. The FRP available with the VIIRS satellite active fire detections indicate that the fires burned less intensely from the beginning to the end of this three day period, the nighttime fires generally burned more intensely than the daytime fires, and the most intensely burning fires occurred over Lahaina likely due to the high fuel load in the buildings compared to the vegetation that burned elsewhere. The MODIS 500 m burned area product was too coarse to map most of the 18 burned areas that were mapped unambiguously at 3 m resolution with PlanetScope and covered 29.60 km2, equivalent to about 1.6% of Maui. This study highlights the limitations of systematically derived satellite fire products for assessment before, during and after wildfire disaster events such as those experienced over Maui. The needs for future fire monitoring of wildfire disaster events, and the recommendation for a fire monitoring satellite constellation, are discussed.

Social and Cultural Hazards, from the 3.11 Disaster through Today’s Global Warming: Shifting Conceptions of the Soma Nomaoi Cavalry Event in Fukushima, Japan

This case study is an anthropological reflection on the impact of multiple disaster events on the culture and economy of the Hamadōri coastal area of Fukushima, Japan. The 2011 Tōhoku earthquake, tsunami, and nuclear meltdown; the pandemic of 2020; and today’s global warming have affected this area’s economic, touristic, and cultural practices, such as the Soma Nomaoi Calvary tradition. Outcomes exemplify the concept of punctuated entropy: a permanent decline in the adaptive flexibility of a human cultural system to the environment brought on by the cumulative impact of periodic disaster events. In the case of Fukushima, efforts to mitigate and recover from these closely occurring disaster events have been only partially successful, and the outcomes provide profound lessons learned regarding the complexity of the recovery process when deep-seated and sustaining cultural practices are disrupted or lost.

The difficulty of nuclear emergency prevention measures during natural disasters: ongoing challenges in Japan

It is crucial to anticipate nuclear emergency scenarios and implement effective measures. Japan’s climate and topography make it vulnerable to natural disasters; thus, it is necessary to address compounding and cascading disaster scenarios involving the simultaneous occurrence of natural and nuclear disasters. On 1 January 2024, an earthquake hit the Noto region of Ishikawa Prefecture, resulting in damage to the area around the Shika Nuclear Power Plant, located 90 km from the epicenter. This earthquake revealed that, in the event of a complex disaster, it is possible that residents living within 30 km of the Shika Nuclear Power Plant will be completely unprepared for a nuclear disaster. In the event of a complex disaster, it is crucial to implement appropriate countermeasures while balancing responses to both nuclear and natural disasters and optimizing radiation disaster prevention measures.

Thermoluminescence characterization of flooring tiles from Malaysia for potential use in retrospective dosimetry

In the event of natural disasters or technical malfunctions at nuclear sites, the surrounding media, including individuals, may be subject to unforeseen radiation exposure. Thus, it is not possible to determine the precise level of radiation exposure that each person has received in this scenario. To handle such a problem, the use of retrospective dosimetry can offer an accurate evaluation of the radiation dose acquired as a result of such incidents. This information enables the development of suitable remedial actions. Normally, in the region that has been impacted, several artifacts can serve as natural dosimeters. The study involved the utilization of locally available seven different brands of flooring tiles in Malaysia for the possibility of being used in retrospective dosimetry through exposures to 60Co gamma-rays within a range of 10–200 Gy. To understand the luminescence features of this material, a convenient thermoluminescence (TL) technique has been employed. The dosimetric features of the samples were investigated, including their effective atomic numbers (Zeff), reproducibility, glow curve, dose response, sensitivity, linearity index, energy dependency and fading, which is representative of the usual TL method. Additionally, by analyzing the glow curves of the irradiated samples together with the peak shape method and the initial rise methods, several kinetic parameters, including the kinetic orders (b), activation energy (E) or trap depth, frequency factor (s) or escape probability, and trap lifetime (τ) were calculated. Different kinds of tiles exhibit distinct scenarios in terms of kinetic characteristics. T1 sample exhibits the highest degree of sensitivity and a highly linear dose response among the tile samples that were evaluated. Also, T1, T3, and T5 samples show minimal loss of TL intensity (∼38–39%) one year after irradiation, making them ideal for use as a retrospective dosimeter.

Deteksi Pola Kejadian Bencana Menggunakan Algoritma Naïve Bayes di Kabupaten Boyolali

Boyolali Regency, which is geographically located between Mount Merbabu and the still active Mount Merapi, does have high potential as a disaster-prone area. According to data from the Boyolali Regional Disaster Management Agency, during the period from 2022 to 2023, 936 disaster events were recorded in the region. These disasters have a devastating impact on ecosystems and the environment, causing significant material losses, causing psychological disorders, and even threatening human safety. In this context, detecting patterns of disaster events is an important step in disaster mitigation and management efforts. This research was conducted with the aim of understanding patterns of disaster events based on data collected by BPBD Boyolali. The method used is the Naive Bayes algorithm, which has proven to be a powerful tool in data analysis. The results of this research show a high level of accuracy of 92.52%, precision of 92.68%, and recall of 91.64% using the Naive Bayes algorithm. This indicates that the algorithm is effective in recognizing and classifying patterns of disaster events based on existing data. Thus, the results of this research can provide a valuable contribution to the development of an early disaster detection system, as well as assist the authorities in designing more effective mitigation strategies to protect the community and environment in Boyolali Regency.

Inventory of aqueous and sediment-associated organic pollutants released by the 2021 flood in the Vicht–Inde catchment, Germany

BackgroundThe European flood that occurred between July 13th and 16th 2021, was a natural disaster that caused significant damage in Central European countries, including Germany, Netherlands, Belgium, and Luxembourg. This disaster resulted in the highest number of fatalities from a natural disaster in Germany during the twenty-first century, with over 180 people losing their lives and causing damages exceeding 30 bn€. The flood caused severe destruction in small mountainous river systems such as the Vicht and Inde rivers, particularly in the German state of North Rhine-Westphalia. In addition, the flood caused a significant release of pollutants, including old burdens from the former mining area of Stolberg.ResultsTo assess the extent of pollution caused by the flood in the affected floodplains and urban areas, this study was conducted to inventorize organic contaminants identified through a non-target screening in water and sediment samples taken immediately after the disastrous flood event. In total, 56 individual contaminants were identified from the water samples, including substances derived from urban effluents, such as personal care products, cosmetics, odors, technical additives, pharmaceuticals and surfactants. The analysis of sediment samples revealed different types of environmentally hazardous contaminants, such as petrogenics, urban effluent and wastewater indicators, chlorinated industrial compounds, and pesticides. This diverse range of pollutants and their broad dispersion across various environments in the catchment is attributed to the dynamic nature of the flood.ConclusionThe inventory of identified organic contaminants raises long-term environmental concerns and potential health implications for the flood-affected Vicht–Inde region.

Long-Term Response of Floodplain Meadow Normalized Difference Vegetation Index to Hydro-Climate and Grazing Pressure: Tamir River Plains, Mongolia

The greenery of floodplain meadows in arid regions, such as Mongolia, is influenced by climate, hydrology, and land use. In this study, we analyzed the NDVI (Normalized Difference Vegetation Index) of two floodplain meadows located along the South Tamir and Tamir Rivers using LANDSAT images. Our goal was to observe NDVI spatial changes, variations, and mean values in mid-August every six years from 1991 to 2015 and to identify the factors driving these differences. To achieve this, we conducted variance analysis to identify changes in NDVI and implemented Principal Component Analysis to determine the influence of hydro-meteorological factors and grazing intensity. Our findings indicate a significant decrease in greenness, as measured by pixel-scale NDVI, during the late summer period. This decrease was consistently observed, except for a series of harsh winters that followed relatively dry summers, resulting in a disastrous event called dzud, which led to the death of livestock. The decrease in NDVI was amplified by lower precipitation in June, higher temperatures and wind speed in July, and increased precipitation in August, along with a higher frequency of days with convective rain. Our findings have important implications for managing grazing in Mongolia’s grasslands, promoting sustainable land use, and mitigating sandstorms. The variance and average values of NDVI at the pixel level can serve as reliable markers of sustainable pasture management in areas where other vegetation measures are limited.

Analysis of disaster events in Serang City seen from the number of victims and damage

Analysis of disaster events in Serang City seen from the number of victims and damage