Occurrence Of Disasters Research Articles

A scenario study on disaster preparedness behaviours

Abstract Europe has faced several crises over recent years, including the COVID-19 pandemic and the war in Ukraine. Additionally, the climate crisis has led to an increase in the occurrence of extreme weather events and natural disasters. Getting citizens on board when it comes to being prepared for the changing risk landscape in the EU is essential in building societal resilience. The importance of the population’s preparedness to deal with disasters has also been underlined by the Commission’s identification of the Union Disaster Resilience Goals (2023/C 56/01). One of these goals focuses on increasing risk awareness and the adoption of risk prevention and preparedness measures among the population. Research has shown that simply advising people of potential risks in their region is not necessarily sufficient to motivate them to prepare. Yet, preparation is key to an adequate responses in emergency situations and improving preparedness may thus be a powerful tool to support the population’s ability to respond in times of crisis. The EC JRC’s Competence Centre on Behavioural Insights will present a study conducted in five member states (N = ∼1000 per country), which investigates the effect of envisioning a disaster situation, and therefore explicitly eliciting personal affectedness, on individuals’ self-reported preparedness intentions and preparedness behaviours. The study uses multiple measures for intentions and preparedness behaviours, including click-through to external information and an incentivised preparedness game.

Linking regional economic impacts of temperature-related disasters to underlying climatic hazards

Abstract Temperature-induced disasters lead to major human and economic damage, but the relationship between their climatic drivers and impacts is difficult to quantify. In part, this is due to a lack of data with suitable resolution, scale and coverage on impacts and disaster occurrence. Here, we address this gap using new datasets on subnational sector-disaggregated economic productivity and geo-coded disaster locations to quantify the role of climatic hazards on economic impacts of temperature-induced disasters at a subnational scale. Using a regression-based approach, we find that the regional economic impacts of heat-related disasters are most strongly linked to the daily maximum temperature (TXx) index. This effect is largest in the agricultural sector (6.37% regional growth rate reduction per standard deviation increase in TXx anomaly), being almost twice as strong as in the manufacturing sector (3.98%), service sector (3.64%), and whole economy (3.64%). We also highlight the role of compound climatic hazards in worsening impacts, showing that in the agriculture sector, compound hot-and-dry conditions amplify the impacts of heat-related disasters on growth rates by a factor of two. In contrast, in the service and manufacturing sectors, stronger impacts are found to be associated with compound hot and wet conditions. These findings present a first step in understanding the relationship between temperature-related hazards and regional economic impacts using a multi-event database, and highlight the need for further research to better understand the complex mechanisms including compound effects underlying these impacts across sectors.

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).

Operational Stress Injuries in Disaster Responders: A Framework for Supporting Mental Health in Climate Crisis.

The rising occurrence of natural disasters linked to climate change has drawn the attention of disaster response leaders to the significance of addressing operational stress injuries among disaster response personnel. We define and describe operational stress injuries in this workforce and explore theoretical frameworks that can inform the development of programs and interventions to mitigate these effects. We aim to establish a conceptual framework for understanding an operational stress injury by introducing a model specific to disaster responders. We also emphasize strategies that can be employed within a stress injury awareness framework to promote the mental well-being of those who respond to climate-related disasters.

An Investigation into the Susceptibility to Landslides Using Integrated Learning and Bayesian Optimization: A Case Study of Xichang City

In the middle southern section of the Freshwater River–Small River Fault system, Xichang City, Daliang Prefecture, Sichuan Province, is situated in the junction between the Anning River Fault and the Zemu River Fault. There has been a risk of increased activity in the fault zone in recent years, and landslide susceptibility evaluation for the area can effectively reduce the risk of disaster occurrence. Using integrated learning and Bayesian hyperparameter optimization, 265 landslides in Xichang City were used as samples in this study. Thirteen influencing factors were chosen to assess landslide susceptibility, and the BO-XGBoost, BO-LightGBM, and BO-RF models were evaluated using precision, recall, F1, accuracy, and AUC curves. The findings indicated that after removing the terrain relief evaluation factor, the four most significant factors associated with landslide susceptibility were NDVI, distance from faults, slope, and distance from rivers. The study demonstrates that the AUC value of the BO-XGBoost model in the study area is 0.8677, demonstrating a better generalization ability and higher prediction accuracy than the BO-LightGBM and BO-RF models. After Bayesian optimization of hyperparameters, the model offers a significant improvement in prediction accuracy.

Influencing factors and predictions of carbon emissions for the chemical industry in China

As global warming increases the frequent occurrences of natural disasters, the reduction of carbon emissions has become an important issue around the world. The chemical industry is an important source of carbon emissions in China. The carbon emissions of the chemical industry are calculated from 2000 to 2019 by using the emission factor method. The logarithmic mean divisia index (LMDI) method is exploited to analyze the factors that influence carbon emissions, and the emissions variations are attributed to the contributions of carbon intensity, energy structure, energy intensity, industrial value-added rate, per capita industrial output value, and industrial scale. The results of decomposition show that per capita industrial output value is the main driving factor, and energy intensity is the main inhibiting factor of the chemical industry’s carbon emissions. In order to quantify the variation of carbon emissions, the extended stochastic impacts by regression on population, affluence, and technology (STIRPAT) model is constructed and examined. Using the STIRPAT model, the basic scenario and energy intensity control scenario are set, and the carbon emissions are predicted, which shows that under a strict energy intensity control scenario, carbon emissions may reach a peak around 2031. The factors influencing the decomposition and prediction of carbon emissions should be helpful in reducing the carbon emissions of the chemical industry in China.

Atherosclerotic vascular lesion in the coal industry workers of average and high fatal risk groups

Introduction. Experts do not take into account the presence of subclinical atherosclerosis when stratifying risk on the SCORE scale, however, it is the main substrate for the occurrence of cardiovascular disasters. Most primary cardiovascular events occur in low cardiovascular risk groups, which requires timely detection of atherosclerosis and preventive measures to preserve the working population's ability to work and reduce the economic burden of treatment of cardiovascular diseases. The study aims to research the incidence of atherosclerosis in workers of the main professions of the coal industry in the South of Kuzbass in groups of medium and high risk of fatal events on the SCORE scale. Materials and methods. We divided the subjects into two groups using the SCORE scale: high-risk (main) — from 5 to 9%, which included 60 people, and moderate-risk group — from 2 to 4% (comparison) — 70 employees. The average age of the first was 50.98±0.43 years, the comparison group was 49.96±0.35 years (p=0.070). The scientists examined the indicators of blood pressure, lipid, carbohydrate and purine metabolism, and performed an ultrasound examination of the main arteries. Results. The incidence of atherosclerotic plaques in at least one vascular basin was 80.0% in the main group and 78.6% in the comparison group, p=0.845. The frequency of carotid artery atheromas in the high-risk group on the SCORE scale (52.7%) did not significantly differ from the comparison group (54.3%), p=0.862; a similar pattern in the arteries of the lower extremities: 80.0% vs. 70.0%, respectively, p=0.203. The frequency of carotid artery atheromas in the high-risk group on the SCORE scale (52.7%) did not significantly differ from the comparison group (54.3%), p=0.862; a similar pattern in the arteries of the lower extremities: 80.0% vs. 70.0%, respectively, p=0.203. The combination of thickening of the intima of vessels and the presence of atheromas tended to be more frequent in the main group — 83.6% compared with those in the comparison group — 70.1%, p=0.076 Limitations. The study is limited by the number of examined patients who underwent periodic medical examination at the Scientific Research Institute of Complex Problems of Hygiene and Occupational Diseases. Conclusion. The incidence of atherosclerosis among employees of coal enterprises turned out to be very high and did not statistically differ in the groups of medium and high fatal risk stratified on the SCORE scale, which requires more thorough screening and measures of enhanced angioprotective prevention, even in people with a low risk of fatal cardiovascular disasters. Ethics. All the subjects signed an informed consent to participate in the study, the Protocol of which corresponded to the requirements of the Bioethical Committee of the Research Institute of Complex Problems of Hygiene and Occupational Diseases, executed in accordance with the Helsinki Declaration of the World Medical Association "Ethical Principles of conducting medical research with human participation as a subject" as amended in 2013 and the "Rules of Good Clinical Practice" approved by the order Ministry of Health of the Russian Federation No. 200n dated April 1, 2016.

AVA-YOLO: image-based multiscale feature fusion enhanced perception model for snow avalanche detection

Abstract The global climate change has led to frequent occurrences of snow avalanche disasters. However, the significant variations in scale and shape during the avalanche process, and complex background imagery pose significant challenges to automated detection efforts. There is an urgent need to combine advanced deep learning technology to research automatic detection and recognition of avalanches in the field. In this paper, a novel deep learning model based on YOLOv8 improved multi-scale detection called AVA-YOLO is proposed to solve this problem. In AVA-YOLO, a key component, AKA (AKConv Combined Attention) module was designed and developed. This module combines the deformable convolutional properties of AKConv with the state-of-the-art self-attention module Exponential Moving Average, aiming to better perceive the feature map information of different shaped avalanches and to enhance the global relevance, thus improving the utilization of the information. Secondly, a new multi-scale sensing network structure was designed by increasing the number of detection heads to four and introducing the AKA module into the key positions of the network, while the association between model layers was newly designed to enhance the fusion of shallow and deep information to improve the detection accuracy. Experimental results demonstrated the effectiveness of AVA-YOLO, achieving 95.7% mAP50 and 75.6% mAP50:95 detection accuracies, as well as an F1 score of 0.92. Finally, a number of experiments were conducted to demonstrate the superior performance of the proposed model in comparison to other versions of YOLO, which will further exploit the potential of webcams as an underutilized technical capability in snow avalanche intelligence and portable monitoring.

Experimental study of type-I crack propagation in rock monitored by fiber Bragg grating

The occurrence of rock mass engineering disasters can be reduced significantly by obtaining the key information in the process of rock internal fracture accumulation and evolution through advanced monitoring means and taking certain preventive measures. Through fiber Bragg grating monitoring tests of the type-I crack propagation in rock, the spectral characteristics of the fiber Bragg grating in the process of rock failure were analyzed, the bandwidth expansion was extracted to characterize the local tensile strain during the crack propagation process, and the damage variable based on the bandwidth expansion was calculated to describe the type-I crack propagation process. The research results showed the following: (1) The fiber Bragg grating generated a non-uniform local tensile strain during type-I crack propagation. The bandwidth expansion of the wave spectrum was positively correlated with the local tensile strain. The change in the characteristics of the wave spectrum could provide the local real strain in the process of crack propagation. (2) During the steady propagation of the type-I crack, the bandwidth increased obviously, while the number of acoustic emission events was relatively small, and most of them were in the process zone. The gentle increase in the bandwidth corresponded to the unstable growth stage of the type-I crack, which could be used as a precursor of rock sample instability. During loading, the maximum tensile strain of the type-I crack was 6 %, while the maximum shear strain reached 1.8 %, primarily attributed to tensile failure. The average value of fracture toughness KIC, based on the equivalent linear elastic fracture mechanics model, was determined to be 1.21 MPa·m1/2, and the average value of fracture energy Gf was calculated as 57.7 N/m. The variation of peak reflectivity in fiber Bragg grating was associated with the closure of pre-existing defects and the propagation of microcracks in rocks, resulting in unstable shear strain at crack extension sites. (3) The damage variable D defined by the bandwidth broadening began to become concentrated and accumulated in the crack steady growth stage, and it increased slowly in the crack intensification and evolution stage. The grating was sensitive to changes when it was pasted to the surface at 90°, but it could easily fracture or become unable to capture spectral signals in time. Based on a comprehensive comparison, the monitoring effect was the best when the grating was pasted at 60° relative to the crack.

Catastrophe Information Characteristics and Prevention Measures of Water Inrush in Tunnel Approaching Fault with Different Water Pressure

In order to ensure the safety of the tunnel approaching the fault and prevent water inrush disasters, and then take reasonable protective measures, a fault-tunnel-surrounding rock is established by using a three-dimensional (3D) discrete element numerical analysis method, which takes into account the fluid-structure coupling effect. Based on the method of control variables, the catastrophe information characteristics of displacement and water pressure of the surrounding rock of the tunnel face and the corresponding characteristics of changes before the occurrence of water inrush disasters were studied under different fault water pressures during the excavation of the tunnel approaching the water-rich fault. The results show that, during excavation at the same step, displacement and its magnitude in the surrounding rock escalate as fault water pressure increases. The maximum pressure of the water in the surrounding rock is also constantly increasing. As tunnel excavation progresses, at constant fault water pressure, longer excavation distances result in greater axial displacement of the surrounding rock mass and increased water pressure at corresponding positions within the surrounding rock, leading to higher magnitude increases. As excavation proceeds, the displacement and water pressure in the surrounding rock and the increase of its amplitude continue to increase. Pre-reinforcement grouting techniques and pipe umbrella support systems that are very effective protective measures can be determined by a comprehensive approach integrating advanced geological forecasting methods, real-time water pressure detection, and the analysis of stress-strain and seepage pressure field variations in the surrounding rock mass.

Post-traumatic growth promotes resilience development: A Longitudinal Mediation Model

BackgroundThe frequent occurrence of global disasters poses unprecedented challenges to nursing practice. The frontline nurses in disaster relief are exposed to these events and bear significant levels of stress and psychological distress. Resilience and posttraumatic growth (PTG) are essential protective factors that contribute to sustaining their mental health. The purpose of this study was to determine the directional relationship between resilience and PTG using a cross-lagged design. Furthermore, employing longitudinal mediation to test whether the T1 resilience of frontline nurses would promote the development of T3 resilience through the mediating role of T2 PTG. MethodsA total of 258 frontline nurses were selected as subjects. They completed self-reported measurements in three periods. The present study was conducted using a cross-lagged panel model and a longitudinal mediation model. ResultsThe results of cross-lagged path analysis from T2 to T3 showed that PTG could positively predict the development of resilience (β = 0.235, p < 0.001). Resilience did not positively predict the development of PTG (p > 0.05). The analysis of mediating effect results showed that the development of PTG at T2 mediated the relationship between resilience from T1 to T3. LimitationsFindings may be limited by self-report, recall bias of resilience before the epidemic and short tracking frequency. ConclusionsThese results can identify individuals with an increased risk of low resilience under disaster and the mediating role of posttraumatic growth in promoting the development of nurses' resilience, which provides a theoretical basis for psychological crisis intervention and the resilience promotion plan for posttraumatic growth under disaster events.

Trust a few: Natural disasters and the disruption of trust in Africa

Individuals are at their most mental plasticity in their impressionable years (ages 18–25 years) forming long-term attitudes and behaviours essential to functioning in a society, such as trust. In this paper we ask how exposure to natural disasters within the impressionable years may affect the formation of trust by matching data from over 1,000 disaster occurrences with data from 88,670 individuals across 36 African nations. Exploiting the frequency of disaster exposure across the impressionable years, we show that disaster exposure has a negative and significant association with generalized trust. Additionally, we show that disasters experienced during the impressionable years have an impact on other dimensions of interpersonal and institutional trust. Our findings are robust to a battery of tests and add to the evidence base on the lasting impacts of natural disasters on individuals and societies.

Estimation of rock mass permeability using relaxation time and P‐wave velocity

AbstractDue to the inherent unpredictability of geological conditions, tunnelling operations are often at risk of encountering water inrushes. Such incidents can lead to construction delays, impose financial strains and pose significant safety threats to the workers involved. Water‐bearing geological formations are the main triggers for such incidents, with factors such as the positioning, water quantity and permeability distribution of these formations being key to predicting the occurrence and severity of water inrush disasters. By leveraging the complex interplay among relaxation time, P‐wave velocity and permeability within the rock's physical properties, a series of indoor tests were conducted on 40 artificial reef limestone cores to extract the necessary parameters. Through the analysis of the data, the comprehensive permeability prediction model was established, and the correlation coefficient was 0.9420 between the model's predictions and actual measurements. At the same time, through theoretical and mechanism analysis, the relationship between permeability and relaxation time and the relationship between permeability and P‐wave velocity were analysed. Finally, 10 natural reef limestone samples were used to verify the accuracy of the model. The prediction model enables an accurate evaluation of tunnel permeability, thus providing a scientific basis for the mitigation of tunnel water inrush hazards.

Geospatial and Temporal Patterns of Natural and Man-Made (Technological) Disasters (1900–2024): Insights from Different Socio-Economic and Demographic Perspectives

This pioneering study explores the geospatial and temporal patterns of natural and human-induced disasters from 1900 to 2024, providing essential insights into their global distribution and impacts. Significant trends and disparities in disaster occurrences and their widespread consequences are revealed through the utilization of the comprehensive international EM-DAT database. The results showed a dramatic escalation in both natural and man-made (technological) disasters over the decades, with notable surges in the 1991–2000 and 2001–2010 periods. A total of 25,836 disasters were recorded worldwide, of which 69.41% were natural disasters (16,567) and 30.59% were man-made (technological) disasters (9269). The most significant increase in natural disasters occurred from 1961–1970, while man-made (technological) disasters surged substantially from 1981–1990. Seasonal trends reveal that floods peak in January and July, while storms are most frequent in June and October. Droughts and floods are the most devastating in terms of human lives, while storms and earthquakes cause the highest economic losses. The most substantial economic losses were reported during the 2001–2010 period, driven by catastrophic natural disasters in Asia and North America. Also, Asia was highlighted by our research as the most disaster-prone continent, accounting for 41.75% of global events, with 61.89% of these events being natural disasters. Oceania, despite experiencing fewer total disasters, shows a remarkable 91.51% of these as natural disasters. Africa is notable for its high incidence of man-made (technological) disasters, which constitute 43.79% of the continent’s disaster events. Europe, representing 11.96% of total disasters, exhibits a balanced distribution but tends towards natural disasters at 64.54%. Examining specific countries, China, India, and the United States emerged as the countries most frequently affected by both types of disasters. The impact of these disasters has been immense, with economic losses reaching their highest during the decade of 2010–2020, largely due to natural disasters. The human toll has been equally significant, with Asia recording the most fatalities and Africa the most injuries. Pearson’s correlation analysis identified statistically significant links between socioeconomic factors and the effects of disasters. It shows that nations with higher GDP per capita and better governance quality tend to experience fewer disasters and less severe negative consequences. These insights highlight the urgent need for tailored disaster risk management strategies that address the distinct challenges and impacts in various regions. By understanding historical disaster patterns, policymakers and stakeholders can better anticipate and manage future risks, ultimately safeguarding lives and economies.

A Literature Study of Emergency Material Dispatch Models and Optimisation in the Context of Natural Disasters

The recurrent occurrence of natural disasters has brought into sharp focus the urgent need for the expedient distribution of emergency materials. In the aftermath of a disaster, the swift and precise distribution of essential emergency supplies is of the utmost importance in ensuring the basic survival needs of the affected population are met, minimising the impact of the disaster and maintaining social stability. In order to provide a comprehensive overview of the research progress in the field of emergency supplies dispatching after natural disasters, we employ the knowledge graph analysis software VOSviewer to investigate the optimisation issues pertaining to the emergency supplies dispatching model, taking into account the inherent uncertainty, from a multi-objective standpoint and across a range of algorithms. A total of 375 literature sources were included in the China Knowledge Network database for review. The research objectives, methods and conclusions of the representative literature on hot spots at home and abroad were subjected to analysis. The review examines the research objectives, methodologies and conclusions of the most prominent literature on the subject, both domestic and international, and analyses the current status of research on the scheduling of emergency materials in the context of natural disasters. By synthesising these findings, we aim to identify gaps in existing research and suggest directions for future endeavours to promote a more resilient and effective emergency management system that can respond rapidly to the ever-changing needs of disaster-affected communities.

The ERMES chatbot: A conversational communication tool for improved emergency management and disaster risk reduction

The increased occurrence and severity of worldwide disasters, exacerbated by the impact of climate change, pose significant challenges to emergency management, calling for novel and more effective Disaster Risk Reduction (DRR) approaches. Despite the phenomenological differences of diverse hazards and the heterogeneity of local policies and governance structures, effective communication systems are needed to better manage the available resources in all phases of the emergency management cycle, especially in the case of cross-border events. In this work, we present the design, implementation, and validation of a novel tool for emergency management, i.e., the ERMES Chatbot: a mobile-based conversational communication system developed to facilitate real-time bidirectional communication between control centres, in-field forces, and citizens. The methodology used to create the ERMES Chatbot is grounded on the user-centred design approach, which entails end-user involvement in all key phases of the development, including the definition of the end-user requirements, the technical design, and the final validation of the proposed solution. The co-design approach involved several organisations belonging to the DRR domain, which includes a gamified experience for citizens and is iteratively validated through four in-field demonstration events based on realistic emergency scenarios with the involvement of emergency practitioners, volunteering organisations, and citizens.

An exploratory tag map for attributes-in-space tasks

Geo-text data, which combine geographical locations with textual information (e.g., geo-tagged tweets), are typically visualized using tag maps. Since tags are rich in attribute information, tag maps are an intuitive method of visualizing how attribute domains carried by tags vary across space. However, users may be interested not only in the overall spatial distribution of tags but also in exploring detailed attributes-in-space analyses, such as examining how a subclass of attribute domains is distributed globally or checking whether all attribute subclasses exhibit the same global distribution pattern. To date, the methods for representing tags with visual encoding (e.g., size, color) to extend various attributes-in-space tasks to support exploratory analysis remain unclear. In this work, we extended tag maps to support exploratory analysis by distinguishing space searching into local or global spaces and attribute domains into within or between attribute classes, supporting four types of attributes-in-space tasks: global-within, local-within, global-between, and local-between tasks. We evaluated our exploratory tag map through two case studies: investigating major disaster occurrences from 1981 to 2020 and examining the leading causes of death in 2000 and 2019 for Spain, France, Germany and Italy. We used eye-tracking and a questionnaire to evaluate our exploratory tag map for comparison. Both methods had similar self-reported usability scores in terms of aesthetics, density, layout, and legibility. However, our exploratory tag map was more effective and efficient and had a lower cognitive load.

Remote Sensing Data Assimilation in Crop Growth Modeling from an Agricultural Perspective: New Insights on Challenges and Prospects

The frequent occurrence of global climate change and natural disasters highlights the importance of precision agricultural monitoring, yield forecasting, and early warning systems. The data assimilation method provides a new possibility to solve the problems of low accuracy of yield prediction, strong dependence on the field, and poor adaptability of the model in traditional agricultural applications. Therefore, this study makes a systematic literature retrieval based on Web of Science, Scopus, Google Scholar, and PubMed databases, introduces in detail the assimilation strategies based on many new remote sensing data sources, such as satellite constellation, UAV, ground observation stations, and mobile platforms, and compares and analyzes the progress of assimilation models such as compulsion method, model parameter method, state update method, and Bayesian paradigm method. The results show that: (1) the new remote sensing platform data assimilation shows significant advantages in precision agriculture, especially in emerging satellite constellation remote sensing and UAV data assimilation. (2) SWAP model is the most widely used in simulating crop growth, while Aquacrop, WOFOST, and APSIM models have great potential for application. (3) Sequential assimilation strategy is the most widely used algorithm in the field of agricultural data assimilation, especially the ensemble Kalman filter algorithm, and hierarchical Bayesian assimilation strategy is considered to be a promising method. (4) Leaf area index (LAI) is considered to be the most preferred assimilation variable, and the study of soil moisture (SM) and vegetation index (VIs) has also been strengthened. In addition, the quality, resolution, and applicability of assimilation data sources are the key bottlenecks that affect the application of data assimilation in the development of precision agriculture. In the future, the development of data assimilation models tends to be more refined, diversified, and integrated. To sum up, this study can provide a comprehensive reference for agricultural monitoring, yield prediction, and crop early warning by using the data assimilation model.

A novel and cost-effective model for cloud energy storage based on a dual storage setup in active distribution grid with resilience consideration

The rising occurrences of natural disasters, terrorist actions, and cyber-attacks that result in extensive, long-lasting, and expensive disruptions have necessitated a shift in focus towards the resilience of electrical grids for network operators. However, the task of designing a resilient network remains complex and costly. One potential solution to bolster resilience is the deployment of battery energy storage devices on the consumer side, known as distributed energy systems (DES). Despite its effectiveness, the high construction costs and lengthy payback period associated with investing in energy storage devices have led consumers to exhibit reluctance in adopting them. Cloud energy storage (CES) is an innovative and cost-effective solution to address those challenges. In the CES platform, investors install storage facilities in the network which can be rented by consumers to fulfill their needs and they become holders of the virtual batteries. By adopting this approach, consumers are relieved from the burden of maintenance, repair, and installation. While a single CES facility offers reduced costs and increased comfort for consumers, it compromises the resilience of the grid when compared to the Distributed Energy Storage (DES) mechanism. In order to bridge this gap, this paper proposes a dual CES model which serves as an intermediate solution between DES and single CES. The dual CES model strikes a balance between the resilience of the grid and cost-effectiveness. It provides a higher level of resilience compared to a single CES and a lower level compared to DES. Additionally, the costs associated with the dual CES model fall between that of a single CES and DES. This model not only increases the profit margin for investors but also enhances the overall comfort and well-being of consumers compared to the single CES. To validate the proposed model, a Mixed Integer Linear Programming (MILP) problem is formulated and simulated on the IEEE 33 bus network. Three cases are considered: DES, single CES, and dual CES. The results indicate that the dual CES reduces consumers' costs by 28 %, losses by 27 %, unsupplied load costs by 45 %, and return on investment by 33 %. Moreover, it increases the stability margin time by 2 intervals and improves robustness by 47 %.

Experimental study on overtopping failure of concrete face rockfill dam

With the frequent occurrence of natural disasters, the problem of dam failure with low probability and high risk has gradually attracted people's attention. This paper uses flume model tests to systematically analyze the overtopping failure mechanisms of concrete face rockfill dam (CFRD) and identify its failure modes. The tests reveal that the longitudinal erosion of the CFRD breach progress through stages of soil erosion, panel failures, and water flow stabilization. Meanwhile, the cross-section breach process involves the evolution of breach size in rockfill materials, including traceable erosion, lateral broadening, and breach morphology stabilization. The fracture characteristics of the water-blocking panel are primarily evident in the flow-time curve. By analyzing the breach morphology evolution processes in longitudinal and cross sections, the flow-time curve can be subdivided into stages of burst flow formation, breach expansion with flow increase, rapid increase of breach flow discharge due to panel failures, and stabilization of breach flow and size. The primary damage process of the CFRD occurs in a cyclical stage of breach expansion, flow increase, panel failure, and rapid discharge. The rigid face plate and granular body structure contribute to partial dam failure, showing a tendency for gradual expansion of the breach. The longitudinal section illustrates dam failure resulting from panel fracture and rockfill erosion interaction, while downstream slopes exhibit failure due to lateral intrusion of rockfill and cyclic instability. These research results can serve as a reference for constructing a concrete CFRD failure prediction model and conducting disaster risk assessments.