Natural Disasters Research Articles

Historical geographies of a Damascan population crisis: Jawlān and Ḥawrān in the late Mamluk - early Ottoman periods

This multidisciplinary study examines the potential causes of a severe and rapid population and settlement decline during the period of transition in the Jawlān and eastern Ḥawrān regions in the province of Damascus. The Jawlān had been part of a relatively small and centralized sultanate in the Mamluk period. However, in the sixteenth century it was incorporated into an empire that ruled over three continents, thus its importance and standing gradually diminished. Newly recovered data from archaeological surveys, two archaeological excavations, fifteenth-century Arabic chroniclers and sixteenth-century Ottoman tax registers (defters) evidence the magnitude of a demographic crisis that occurred in one of the province's most fertile areas at a time when neighboring regions enjoyed continuity, stability and growth. The study conclusions are significant: nomadization in conjunction with an internal, seasonal migration process likely caused the steep decline, and not external migration or sudden demographic decline due to plagues or natural disasters. This shift from sedentary to nomadic or semi-nomadic life lasted for a long time; the vast majority of the area residents were Bedouin, able to adapt quickly to the new reality that combined work in the (mazra'as) with cattle raising.

Streaming disasters on TikTok: Examining social mediated crisis communication, public engagement, and emotional responses during the 2023 Maui wildfire

Short video-sharing platforms, such as TikTok, play a significant role in allowing the public to experience natural disasters vicariously, share information, and coordinate peer-led disaster relief efforts. With emerging platforms like TikTok providing experientially immersive content, the role of digital storytelling in stimulating public engagement and emotions remains underexplored. Drawing from narrative persuasion theory and visual storytelling literature, the current study proposes an integrated framework to examine the storyteller, visual frame, and digital presentation characteristics of TikTok videos in predicting social-mediated public engagement and emotional reactions surrounding a recent natural disaster, the 2023 Maui Wildfire. Using visual content analysis and computational methods, the study analyzed 526 TikTok videos and 59,950 associated comments. Results showed that audiovisual vividness consistently predicted all three types of disaster engagement. In contrast, storyteller type, storytelling frame, and other presentation characteristics inconsistently predicted information consumption intention, information sharing intention, and emotional reactions on TikTok.

Landslide-oriented disaster resilience evaluation in mountainous cities: A case study in Chongqing, China

Natural and human-made disasters are threatening cities around the world. The resilience of cities plays a critical role in disaster risk response and post-disaster recovery. In mountainous cities, landslides are among the most frequent and destructive hazards. This study presents a novel methodological framework for assessing the spatial resilience of mountainous cities specifically against landslides. Focusing on Chongqing in the Three Gorges Reservoir region, this study conceptually divides the disaster resilience of mountain cities to landslides into two dimensions: environmental resilience and social resilience. This study developed a comprehensive database by compiling data from 4,464 historical landslide events, incorporating 17 environmental resilience indicators and 16 social resilience indicators. Random forest (RF) model was employed to evaluate environmental resilience, achieving a high AUC of 0.968 and an accuracy of 97.1 %. Social resilience was assessed by the Analytic Hierarchy Process (AHP), and comprehensive resilience was ranked by the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). Key findings include: (1) Establishing a multi-dimensional resilience indicator system that effectively assesses landslide-oriented resilience in mountainous cities. (2) Comprehensive resilience in mountainous cities exhibit distinct spatial clustering patterns. Regions with lower environmental resilience are mainly characterized by high rainfall and complex terrain. higher social resilience concentrated in city centers, while peripheral regions face challenges due to weaker economies and inadequate healthcare infrastructure. (3) In the future development of mountain cities, comprehensive and sustainable strategies should be adopted to balance the relationship between environmental resilience and social resilience. This study provides a robust framework for disaster prevention and resilience assessment in mountainous cities, which can be applied to evaluate the disaster resistance capabilities of other mountainous cities.

Multi-stage evolution characteristics and particle size effect of sandstone granules subjected to cyclic loads

Broken granules of different sizes and poor bearing capacities produced by repeated mining of coal seams contribute to natural disasters, such as rock strata movement. In this study, mechanical tests were performed on broken sandstone granules to explore their compaction and recrushing characteristics under repeated mining. The change characteristics of the relevant mechanical parameters of sandstone granules with different particle sizes and changes in energy density were examined, and compaction and recrushing mechanisms of sandstone granules under cyclic loading were identified. The results show that an increase in the initial particle size of sandstone granules has a significant effect on the increase in the strain under load. The cumulative dissipation energy corresponding to final crushing was greater for granules with larger initial sizes. The particle size of sandstone was found to be proportional to the fractal dimension after compaction and crushing. The probability of granular particles with larger initial particle sizes being broken into smaller particles is greater, resulting in the porosity attenuating most rapidly during the stress loading process of granular particles under repeated mining. Therefore, the degree of sample breakage was relatively severe. Particles were rearranged after compaction and crushing. Repeated mining results in dynamic evolution of the pore compaction, deformation, and fractur of the rock mass, as well as stable recombination. The study results provide important theoretical support for understanding of the movement mechanism of rock strata in the goaf under repeated mining.

Natural Disasters Highlight the Fragility of Medical Device Supply Chains

Natural Disasters Highlight the Fragility of Medical Device Supply Chains

Low power computation of transoceanic wave propagation for tsunami hazard mitigation

This paper proposes the use of specialized hardware accelerator based on the Field Programmable Gates Array (FPGA) microchip to compute tsunami wave propagation to assess and manage risks of marine natural disasters, namely, tsunami waves caused by underwater earthquakes. After a sufficiently strong seismic event, many countries and research centres launch extensive computations to estimate the tsunami wave parameters in certain parts of the coast to determine if a declaration of a tsunami alarm is warranted. This requires high computating powers which leads to higher energy costs. The paper demonstrates how an FPGA–based special Calculator (architecture of which has been earlier proposed by the authors), installed on a Personal Computer (PC) could be used to calculate the propagation of a tsunami wave over the entire Pacific Ocean, from the subduction zone offshore Kamchatka Peninsula and Kuril Islands to the coast of Chile. Such calculations offer reliable results within a few minutes and make it possible to obtain the distribution of expected tsunami wave heights along the coast. If the obtained results indicate a danger to the population or possible distruction of infrastructure, it becomes paramount to carry out more detailed calculations to accurately estimate the wave parameters at specific locations along the coast where negative consequences are expected. This requires cluster and/or supercomputer systems, which consume significant energy and hence are expensive. In case the modelling results indicate small values of maximum wave heights at populated coastal areas, population of the near–shore regions can be immediately informed about low amplitude tsunami wave; more detailed studies are not needed. This hence leads to noticeable savings in energy consumption. The paper presents a calculation of the propagation of a tsunami wave across the Pacific Ocean on a personal computer using a FPGA–based hardware acceleration of a computer code execution.

Quantitative assessment and dynamic characteristic measurement of regional resilience: from the perspective of post-earthquakes effects

Strong geological disasters have caused persistent losses in society, economy, and ecological environments. Given the unique geographical settings of the stricken areas, their resilience is prone to damage or even loss. Comprehensive risk assessment of natural disasters is the core content and important foundation for building regional resilience. Therefore, conducting dynamic characteristics analysis of resilience in mountainous disaster areas impacted by strong earthquake geological disasters is vital for ensuring the region's high-quality and sustainable development. This article takes the 51 stricken areas of Wenchuan earthquake as the research object. To this end, social, economic and ecological environmental data from 2008 to 2020 was hereby collected. Initially, a regional resilience assessment system based on "socio-economic-ecological environment" was established, considering the long-term and spatial heterogeneity of geological disasters. Secondly, the regional resilience assessment model was constructed using Spectral clustering-genetic algorithm-improved entropy weight method. Following that, the dynamic characteristics of regional resilience were quantitatively analyzed from two aspects, including change velocity state and change rate trend. Finally, based on the regional resilience characteristics, differentiated resilience enhancement strategies were proposed. Collectively, the results revealed that: (1) From a geological disaster standpoint, the risk in post-earthquake disaster areas exhibited a strikingly rapid decline, with the spatial distribution of geological disaster risk being notably higher in the central areas and diminishing towards the peripheries. (2) Overall, the regional resilience of the 51 stricken areas showed a "V-shaped" trend, with a significant upturn since 2012. (3) From the perspective of dynamic characteristics, more counties (cities) presented an upward trend. (4) The 51 stricken areas were hereby divided into the "benchmarking type", the "declination type", the "backward type", and the "potential type". In conclusion, the current study enhances the technical framework for evaluating regional resilience and provides technical support for the construction of resilient cities.

Biobased hydrophobic liquid mulch film from soybean oil and starch for enhanced terraced field cultivation.

Terraced agriculture faces soil loss during rainstorms leading to natural disasters and crop growth impediments. This study describes a novel biobased hydrophobic liquid mulch film comprised of waste soybean oil, starch, and acrylate monomers that can be used to enhance terraced field cultivation. The novel film, optimized at a 3:7 soybean oil to acrylate monomers ratio, exhibited superior spray ability, reduced wicking, and excellent film formation, which are crucial for its effectiveness as a water erosion barrier. The wet state of the SOSA film demonstrated optimal impact resistance, with increased elongation at break and reduced breaking strength compared to its dry state, facilitating seedling emergence. It significantly improved soil moisture retention (4.8-5.7 %) and temperature (0.9-5.6 °C) and boosted maize seed germination by 28 %. Under extreme conditions of a 24° slope and 90 mm/h rainfall, the SOSA film achieved an 80.6 % reduction in soil loss and a 57.4 % increase in pakchoi yield over bare soil. This study's comprehensive analyses confirmed the film's formation mechanism and provided a scientific basis for its practical application performance, highlighting the film's unprecedented success in using waste materials for sustainable terrace farming and its potential as a transformative approach to soil conservation and crop productivity.

Complex network analysis of fossil fuel functional regions in the United States during the period 2017 to 2022

In this paper we use complex network analysis to describe fossil fuel spatial flows among 132 places covering the entire United States in 2017 and in 2022. These spatial flows are for crude petroleum, gasoline, and oil fuels. The analysis shows that all three fuels have different network topology. For all six networks we find major hubs of crude petroleum and its products, gasoline and fuel oils, concentrated in areas with large reserves such as the south-central part of the US. Using modularity, a network cluster identification metric, we show that spatial interactions can be used to delineate functional regions and their differences across fuel types. These functional regions evolve over time in response to the shifting US role as a major producer and net exporter of fossil fuels, expansion of the domestic pipeline network, and increases in fuel production and refinement locations. The modal split of the fuels examined in this paper shows the dominant role pipelines play for crude petroleum, transporting approximately 83 % of tonnage in 2017 and increasing to almost 89 % in 2022. In contrast, gasoline and oil fuels modal split hovers at around 60 % of tonnage transported by tanker truck followed by other modes including pipelines. Our analysis shows geographic clustering of major hubs and their functional regions along the Gulf Coast in Texas and Louisiana. These are in places that are often the locations of natural disasters. This together with the rapid increase of a few hubs as gateways to fossil fuel US exports makes them prime candidates in disrupting fossil fuel supply chains worldwide and amplifies vulnerability of fossil fuel supply chains. The spatial clustering trends shown in this paper provide added evidence of the source of short-term negative impacts in places such as Chicago in Illinois and Corpus Christi in Texas. This offers added information for government intervention.

Effects of Melatonin on Ginseng Leaf Phenotypic and Photosynthetic Properties Following Chilling and Freezing Stress

Spring frost damage is a significant natural disaster, which can lead to large-scale ginseng (Panax ginseng C. A. Mey) yield reduction. To determine chilling and freezing stress damage to ginseng, we designed an experiment using three-year-old potted ginseng plants. We simulated chilling (0±0.3 ℃) and freezing (-2.5±0.3 ℃, -5±0.3 ℃) in an artificial room, with 16±2 ℃ of the room temperature at night as the control, to evaluate their physiological effects on ginseng leaf spectral reflectance, photosynthetic characteristics, and chlorophyll fluorescence parameters. We also investigated the mitigating effects of exogenous melatonin on ginseng leaves exposed to chilling and freezing stress. All ginseng leaves died under -5 ℃ freezing stress and the mortality rate reached 25∼57% under -2.5 ℃ freezing stress, while no plants died under 0 ℃ chilling stress. After -2.5 ℃ and 0 ℃ stress, leaf spectral reflectance in the 750∼1000 nm band was significantly lower than the control; transpiration rate, net photosynthetic rate, intercellular CO2 concentration, and stomatal conductance decreased significantly compared with the control, and there was no significant change in recovery time. The Fv/Fm ratio and qp decreased after low-temperature stress, while NPQ increased. After one week of melatonin application, leaf spectral reflectance in the 600∼650 and 750∼1000 nm bands reflected significant differences between the effects of melatonin application and no application on ginseng leaf chlorophyll content and structure under freezing stress. Ginseng leaf net photosynthetic rate increased by 23.79% and 4.48%, while intercellular CO2 concentration increased by 33.49 and 11.83% under stress at -2.5°C and 0°C. The ginseng leaf chlorophyll fluorescence parameter (Fv/Fm) recovered to 0.8 and above in one week after melatonin application, and qp decreased under -2.5°C and 0°C stress after melatonin application. Additionally, the NPQ values were 11.44% and 4.6% lower in -2.5+MT and 0+MT, respectively, compared to the treatment without MT applied. A sudden drop in temperature to 0 ℃ or even below caused ginseng plant growth inhibition, but melatonin application at a concentration of 100 nmol L−1 had certain mitigating effects on ginseng growth, which provides a theoretical basis and technical support for ginseng production.

Leveraging multimodal deep learning for natural disaster event classification and its damage severity analysis through social media posts

Accurate and timely information is essential for coordinating an effective disaster response. Traditional methods have struggled to efficiently categorize disaster events and assess damage severity due to the variety and complexity of data sources. Previous research has focused on specific tasks, such as information gathering or humanitarian assistance, but has not adequately addressed the assessment of disaster damage severity. This paper proposes a hybrid learning model to improve disaster event classification and damage severity identification. The model combines image and text data in a cooperative way, using ResNet50 to extract features from images and a LSTM with attention mechanism to learn sequences from text. This combination allows for a more contextual and informative representation of the input data. Compared to existing approaches, the proposed multimodal approach achieves significantly better results in disaster event classification. Apart from the proposed model also shows promising outcome for damage severity of disaster. These advancements are especially important for real-world applications such as disaster management and response coordination, where accuracy and reliability are essential. The comprehensive methodology and empirical results presented in this paper demonstrate the effectiveness and potential of using hybrid learning models to leverage multimodal data for unique and sophisticated analytical tasks in disaster scenarios

The differences historical abortion and hemoglobin between hazard volcano eruption

The disaster area volcano is priority for intervention through maternal health is vulnerable group. Previous study estimate severe bleeding /haemorrhage was up to 71.9%, anaemia 51%. The study aimed to know incident of history abortion and haemoglobin in zone hazard risk volcanic eruption after 10th. Study addressed with cross-sectional design. The data history abortion and haemoglobin were collected with medical record in Puskesmas Cangkringan. The hazard level adoption in National Disaster Management Agency/Badan Nasional Penanggulangan Bencana (BNPB) divide 3 zone hazard risks. Data analysis by Health Mapper version 4.3 software and analysis with analysis of variance (ANOVA). The incidence of history abortion higher in zone 2 compare zones 1 and 3 volcanic disaster. The average of haemoglobin higher in zone 1 as 12.54±1.67 compare zones 2 and 3 volcanic disaster. No significant differences history abortion and haemoglobin between zones 1 to 3 volcanic disaster. The study concluded that history of abortion higher in zone 3 compared zones 1 and 2 volcanic disaster. The average of haemoglobin higher in zone 1 compared zones 2 and 3 volcanic disaster. No differences significant of history abortion and average haemoglobin between zones 1 to 3.

Urban forest and sustainability: the historical perspective of Limbe in the Southwest Region of Cameroon

ABSTRACT Projected rapid urbanisation and climate change will increase the risk of natural disasters in fragile coastal African cities. Strategically planning and managing the urban forest can help to build resilience. This study aimed to document urban forest development and sustainability in Cameroon using the city of Limbe as a baseline through the classification of urban forest elements; description of their developmental history and assessment of the level of sustainability to inform strategic planning. Data was collected using a forest ethnology approach. Two groups of stakeholders’ representatives of public institutions (Group 1), Non-Governmental Organisations and educated indigenes (Group 2) provided oral histories of tree planting. Face to face interviews with 15 stakeholders were conducted guided by semi-structured questionnaires. From these results, there are four types of urban forest. Limbe Botanical Garden (LBG) developed during the colonial era was the most prominent. Municipal authorities by law should create different types of urban forest but their actions have been limited to Roadside Trees (RT) planting. The absence of budget, policy and inventory was responsible for the low level of urban forest sustainability. Increasing awareness of the effectiveness of urban forest nature-based solutions in tackling deadly landslides and floods in Limbe can promote urban forest sustainability.

Strengthening food supply resilience: Lean and Lean Six Sigma strategies for disruptions

ABSTRACT Recognizing that the food industry faces a myriad of challenges, ranging from natural disasters to geopolitical conflicts and pandemics, this study explores how Lean, and Lean Six Sigma (L&LSS) can fortify supply chains against such diverse disruptions. While the COVID-19 pandemic serves as a contextual backdrop, our focus is on the broader application of L&LSS methodologies in navigating disruptive events. This study analyzes responses from 120 food firms, utilising structural equation modelling to correlate L&LSS practices with adaptive performance during crises. Our findings indicate that L&LSS not only equip firms to maintain operational continuity amidst disruptions but also positions them to anticipate and adapt to future systemic shocks. By elucidating the positive correlation between L&LSS engagement and crisis resilience, this research contributes a strategic framework to the literature, advocating for a pre-emptive embrace of L&LSS practices as essential to navigating the unpredictable nature of global crises.

Analysis of Mitigation for Landslide Disaster Management in Lumajang Regency, East Java Province

This research aims to analyze the mitigation of natural disaster management, specifically landslides, in Lumajang Regency, East Java Province. Natural disasters are undesirable events, making it crucial for the government and the community to understand the processes and factors leading to such disasters. One significant disaster that requires attention from both the government and the community in Lumajang is landslides, necessitating an understanding of their impacts and management strategies. The alternatives for addressing this issue can involve disaster mitigation through various stages, which are the focus of this study, aimed at reducing the ongoing risks associated with landslide vulnerabilities. This research is descriptive in nature, utilizing a quantitative approach, with subjects comprising individuals who can share their knowledge about the phenomena or cases being studied. The findings indicate that the Regional Disaster Management Agency (BPBD) of Lumajang Regency is responsible for disaster management, including landslides. However, their mitigation stages—such as mapping, inspections, investigations, monitoring, and socialization—have not been effectively implemented to reduce disaster risks. The BPBD tends to act only after a disaster occurs, resulting in a lack of proactive risk reduction through disaster mitigation. There is a pressing need for efforts from various stakeholders to enhance awareness of landslide dangers, including the establishment of an early warning system and community education regarding natural disasters through outreach and education before, during, and after landslide events. Keywords: Mitigation analysis, Disaster, Landslide

A High-Concurrency Tasks Emergency Offloading Framework for Industrial Edge Networks

In the envisioned scenario of smart factories, large-scale distributed intelligent industrial devices generate a substantial number of concurrent heterogeneous industrial tasks. A single device is inadequate to meet the demanding requirements of complex tasks, which necessitate high computational capabilities and low latency in the system. Typically, multi-access edge computing (MEC) is employed to address this challenge. However, industrial edge networks face threats such as network attacks, natural disasters, and hardware failures, posing security risks to the industrial edge network. Therefore, we have designed an emergency offloading framework to enhance its resilience. Based on a task path analysis method with multiple priority levels, we developed an emergency task scheduling pre-classification (ETSPC) algorithm. Experimental results demonstrate that the proposed algorithm significantly reduces the delay of emergency offloading for high-concurrency tasks, with a maximum reduction of up to 19.2%. In extreme scenarios, the task completion rate of the ETSPC algorithm increases by 45.3% compared to the control algorithm.

Decision aids and systematic information processing increase disaster preparedness intentions

ABSTRACT Across the world and fueled by climate change, natural disasters are increasing in their geographic distribution and intensity. The purpose of this study was to use theory, including the heuristic-systematic model, to inform how decision aids may influence persuasion about hurricane preparedness. Results indicate that decision aid selection can serve as a behavioral outcome of systematic information processing and amplify attitude and behavior change effects beyond systematic processing alone. Overall, results suggest that exposure to decision aids can facilitate persuasion about hurricane preparedness.

Mainstreaming disaster risk reduction (DRR) into development: effectiveness of DRR investment in Khyber Pakhtunkhwa, Pakistan

As a low-income country, Pakistan is particularly vulnerable to various natural and human-induced disasters, which have significant consequences for both the environment and human life. A substantial share of the provincial budget is allocated to disaster response and recovery efforts. Prioritizing investment in disaster risk reduction (DRR) is essential to protect lives and assets. Although there is a complex relationship between investments made before and after disasters and their effectiveness in DRR, this dynamic remains insufficiently understood in Pakistan’s Khyber Pakhtunkhwa (KP) province. The current study was designed to analyze the developmental budgeting for DRR in the financial years 2000–2022 in KP province. The Sen’s Slope Estimator and modified Mann-Kendall tests were used to determine the significance trend, while the correlation coefficient test was used to find the correlation between investment in DRR and disaster-induced damages such as deaths, injuries and houses damaged. The study findings reveal that the occurrence of disasters influences post-disaster spending in the KP province, with a significant negative correlation between expenditure and disaster-related damages, implying that increased DRR investment has significantly reduced the consequences of disasters. To minimize vulnerability to future disasters, the province should integrate risk-sensitive planning across all sectoral departments at local, district, and provincial levels, guided by a risk-informed development approach. This proactive strategy would embed disaster resilience within developmental activities, ensuring that each sector aligns with principles of risk reduction and sustainable growth.

Earthquake Environmental Effects: The Case of Late Classical-Hellenistic Helike, Gulf of Corinth, Greece

Human habitat is much controlled by the landscape and its ongoing processes overtime. Some of these processes occur instantaneously and are often triggered by seismic events with a major destructive impact on the human-built environment. Helike, on the southwest shore of the Gulf of Corinth, is a characteristic case of an ancient habitation site bearing witness to repetitious natural disasters from the Early Bronze Age to the Late Antiquity. The Late Classical-Hellenistic site, revived in the Helike plain after the 373 BC earthquake, has been systematically investigated thanks to the multidisciplinary research and excavations of the Helike Project in the last 35 years. This work has significantly enriched the historical seismicity of the region and shed light on past human-environment relationships. The study of the architectural remains excavated by the Helike Project, coupled with geological and soil micromorphological analysis on archaeological soils and sediments of the settlement, demonstrates a constant effort of the Helike people to reconcile with the elements of nature. Our results underline the destruction of a flourishing textile dyeworks operated at the settlement, due to a strong earthquake which triggered extensive morphological changes in a broader area. These changes include co-seismic liquefaction and lateral spreading, and post-seismic changes in the gradient of river channels. The former changes attest to an uplift in the headwater area and subsidence in the lowland plain of the ravine flowing near the ancient site.

Application of the Standardized Precipitation Index for Forecasting Seasonal Drought in Vietnam

Drought is a natural disaster that causes significant damages to the economy, society and environment. Forecasting droughts is crucial for disaster risk management and water security. This study introduces a new method for forecasting seasonal droughts in Vietnam using the Standardized Precipitation Index (SPI) which is based on a combination of dynamic modeling and statistical methods. The precipitation forecast products from regional climate models (RegCM, clWRF) with inputs from Climate Forecast System (CFSv2) were used to calculate the SPI index after statistical calibration. Results show that calibrating model precipitation with statistical methods such as Artificial Neural Networks (ANN) and Multivariate Linear Regression (MLR) significantly improves the precipitation forecast accuracy. Evaluation indices indicate that the SPI calculated with the post-calibration model precipitation has a good reproducibility for drought events, particularly mild droughts. However, there remains some limitations on accurate forecast of the intensity and spatial distribution of moderate drought events in certain regions such as Central Vietnam.