Risk Analysis Using Multi-Source Data for Distribution Networks Facing Extreme Natural Disasters

Distribution networks denote important public infrastructure necessary for people’s livelihoods.However,extreme natural disasters,such as earthquakes,typhoons,and mudslides,severely threaten the safe and stable operation of distribution networks and power supplies needed for daily life.Therefore,considering the requirements for distribution network disaster prevention and mitigation,there is an urgent need for in-depth research on risk assessment methods of distribution networks under extreme natural disaster conditions.This paper accessesmultisource data,presents the data quality improvement methods of distribution networks,and conducts data-driven active fault diagnosis and disaster damage analysis and evaluation using data-driven theory.Furthermore,the paper realizes real-time,accurate access to distribution network disaster information.The proposed approach performs an accurate and rapid assessment of cross-sectional risk through case study.The minimal average annual outage time can be reduced to 3 h/a in the ring network through case study.The approach proposed in this paper can provide technical support to the further improvement of the ability of distribution networks to cope with extreme natural disasters.

Natural Disaster Risk Monitoring for Immovable Cultural Relics Based on Digital Twin

Natural disaster risk monitoring is an important task for disaster prevention and reduction.In the case of immovable cultural relics,however,the feedback mechanism,risk factors,monitoring logic,and monitoring indicators of natural disaster risk monitoring are complex.How to achieve intelligent perception and monitoring of natural disaster risk for immovable cultural relics has always been a focus and a challenge for researchers.Based on the analysis of the concepts and issues related to the natural disaster risk of immovable cultural relics,this paper proposes a framework for natural disaster risk monitoring for immovable cultural relics based on the digital twin.This framework focuses on risk monitoring,including the physical entities of natural disaster risk for immovable cultural relics,monitoring indicators,and virtual entity construction.A platform for monitoring the natural disaster risk of immovable cultural relics is proposed.Using the Puzhou Ancient City Site as a test bed,the proposed concept can be used for monitoring the natural disaster risk of immovable cultural relics at different scales.

Overview on Mangrove Forest Disaster Prevention and Mitigation Functions

This paper provides a comprehensive overview on coastal protection and hazard mitigation by mangroves.Previous stud-ies have made great strides to understand the mechanisms and influencing factors of mangroves’protection function,including wave energy dissipation,storm surge damping,tsunami mitigation,adjustment to sea level rise and wind speed reduction,which are sys-tematically summarized in this study.Moreover,the study analyzes the extensive physical models,based on indoor flume experi-ments and numerical models,that consider the interaction between mangroves and hydrodynamics,to help our understanding of mangrove-hydrodynamic interactions.Additionally,quantitative approaches for valuing coastal protection services provided by man-groves,including index-based and process-resolving approaches,are introduced in detail.Finally,we point out the limitations of previous studies,indicating that efforts are still required for obtaining more long-term field observations during extreme weather events,to create more real mangrove models for physical experiments,and to develop numerical models that consider the flexible properties of mangroves to better predict wave propagation in mangroves having complex morphology and structures.

Rock mass quality prediction on tunnel faces with incomplete multi-source dataset via tree-augmented naive Bayesian network

Rock mass quality serves as a vital index for predicting the stability and safety status of rock tunnel faces.In tunneling practice,the rock mass quality is often assessed via a combination of qualitative and quantitative parameters.However,due to the harsh on-site construction conditions,it is rather difficult to obtain some of the evaluation parameters which are essential for the rock mass quality prediction.In this study,a novel improved Swin Transformer is proposed to detect,segment,and quantify rock mass characteristic parameters such as water leakage,fractures,weak interlayers.The site experiment results demonstrate that the improved Swin Transformer achieves optimal segmentation results and achieving accuracies of 92%,81%,and 86%for water leakage,fractures,and weak interlayers,respectively.A multisource rock tunnel face characteristic(RTFC)dataset includes 11 parameters for predicting rock mass quality is established.Considering the limitations in predictive performance of incomplete evaluation parameters exist in this dataset,a novel tree-augmented naive Bayesian network(BN)is proposed to address the challenge of the incomplete dataset and achieved a prediction accuracy of 88%.In comparison with other commonly used Machine Learning models the proposed BN-based approach proved an improved performance on predicting the rock mass quality with the incomplete dataset.By utilizing the established BN,a further sensitivity analysis is conducted to quantitatively evaluate the importance of the various parameters,results indicate that the rock strength and fractures parameter exert the most significant influence on rock mass quality.

Challenges in Implementing DVI Best Practice Guidelines Due to the Brumadinho Disaster

The broke of a tailings dam from Vale S.A. destroyed a gigantic area killing 270 persons in Brumadinho/Brazil. Organizing activities after a mass disaster is a complex process that requires the involvement of many people and resources in the laboratory. To DNA identification of the victims, a daunting work had to be made by DNA laboratory staff in a collaborative effort with many partners. Efforts to implement good practice guidelines in Disaster Victim Identification (DVI) have revealed several important aspects that need to change in the forensic DNA laboratory. This article highlights the challenges of implementing DVI best practice guidelines in resource-poor settings, but with professionals from different sectors engaged in the same goal of briefly identifying victims and helping families and society.

Risk assessment of coastal flooding disaster by storm surge based on Elevation-Area method and hydrodynamic models:Taking Bohai Bay as an example

The future inundation by storm surge on coastal areas are currently ill-defined.With increasing global sealevel due to climate change,the coastal flooding by storm surge is more and more frequently,especially in coastal lowland with land subsidence.Therefore,the risk assessment of such inundation for these areas is of great significance for the sustainable socio-economic development.In this paper,the authors use Elevation-Area method and Regional Ocean Model System(ROMS)model to assess the risk of the inundation of Bohai Bay by storm surge.The simulation results of Elevation-Area method show that either a 50-year or 100-year storm surge can inundate coastal areas exceeding 8000 km^(2);the numerical simulation results based on hydrodynamics,considering ground friction and duration of the storm surge high water,show that a 50-year or 100-year storm surge can only inundate an area of over 2000 km^(2),which is far less than 8000 km^(2);while,when taking into account the land subsidence and sea level rise,the very inundation range will rapidly increase by 2050 and 2100.The storm surge will greatly impact the coastal area within about 10-30 km of the Bohai Bay,in where almost all major coastal projects are located.The prompt response to flood disaster due to storm surge is urgently needed,for which five suggestions have been proposed based on the geological background of Bohai Bay.This study may offer insight into the development of the response and adaptive plans for flooding disasters caused by storm surge.

A multi-source information fusion layer counting method for penetration fuze based on TCN-LSTM

When employing penetration ammunition to strike multi-story buildings,the detection methods using acceleration sensors suffer from signal aliasing,while magnetic detection methods are susceptible to interference from ferromagnetic materials,thereby posing challenges in accurately determining the number of layers.To address this issue,this research proposes a layer counting method for penetration fuze that incorporates multi-source information fusion,utilizing both the temporal convolutional network(TCN)and the long short-term memory(LSTM)recurrent network.By leveraging the strengths of these two network structures,the method extracts temporal and high-dimensional features from the multi-source physical field during the penetration process,establishing a relationship between the multi-source physical field and the distance between the fuze and the target plate.A simulation model is developed to simulate the overload and magnetic field of a projectile penetrating multiple layers of target plates,capturing the multi-source physical field signals and their patterns during the penetration process.The analysis reveals that the proposed multi-source fusion layer counting method reduces errors by 60% and 50% compared to single overload layer counting and single magnetic anomaly signal layer counting,respectively.The model's predictive performance is evaluated under various operating conditions,including different ratios of added noise to random sample positions,penetration speeds,and spacing between target plates.The maximum errors in fuze penetration time predicted by the three modes are 0.08 ms,0.12 ms,and 0.16 ms,respectively,confirming the robustness of the proposed model.Moreover,the model's predictions indicate that the fitting degree for large interlayer spacings is superior to that for small interlayer spacings due to the influence of stress waves.

Climate Change-Related Disaster Risk Events in Togo: A Systematic Review

Togo is facing significant climate challenges that have profound consequences for its environment, economy, and population. This study provides an overview of various climate phenomena affecting Togo and highlights potential adaptation strategies. We used the inclusion and exclusion criteria (PRISMA) to search both French and English articles on climate change-related disaster risk events in Togo through Google Scholar, Directory of Open Access Journals (DOAJ), and PubMed databases using the keywords “Climate Change”, “Floods”, “Drought”, “Coastal erosion”, “High winds”, “Epidemy”, Heatwaves”, and “Air pollution”. Twenty-five articles from 2000-2023 were included in this study after applying different criteria. Droughts, floods, coastal erosion, food and crop productivity loss, heatwaves, spread of vector-borne diseases, air pollution, and high winds are among the climate phenomena discussed. These challenges are driven by climate change, altering precipitation patterns, increasing temperatures, and rising sea levels. Drought, floods, coastal erosion, loss of food and crop productivity, spread of vector-borne diseases, air pollution and heatwaves are the most climate risks experienced by Togo. Drought contributes to decreased plant cover, water scarcity, and changes in the water and energy balance. Floods cause property damage, health risks, and disruptions to livelihoods. Coastal erosion threatens coastal communities, infrastructure, and ecosystems. Adaptation strategies include early warning systems, improved water management, sustainable agriculture, urban and health planning, and greenhouse gas emissions reduction. Drought-resistant crops, mosquito control, and clean energy adoption are essential.

Multi-source heterogeneous data access management framework and key technologies for electric power Internet of Things

The power Internet of Things(IoT)is a significant trend in technology and a requirement for national strategic development.With the deepening digital transformation of the power grid,China’s power system has initially built a power IoT architecture comprising a perception,network,and platform application layer.However,owing to the structural complexity of the power system,the construction of the power IoT continues to face problems such as complex access management of massive heterogeneous equipment,diverse IoT protocol access methods,high concurrency of network communications,and weak data security protection.To address these issues,this study optimizes the existing architecture of the power IoT and designs an integrated management framework for the access of multi-source heterogeneous data in the power IoT,comprising cloud,pipe,edge,and terminal parts.It further reviews and analyzes the key technologies involved in the power IoT,such as the unified management of the physical model,high concurrent access,multi-protocol access,multi-source heterogeneous data storage management,and data security control,to provide a more flexible,efficient,secure,and easy-to-use solution for multi-source heterogeneous data access in the power IoT.

Mapping Climate Services for Disaster Risk Management: A Systematic Review and Research Gaps from a Policy Process Perspective

Climate services (CS) are crucial for mitigating and managing the impacts and risks associated with climate-induced disasters. While evidence over the past decade underscores their effectiveness across various domains, particularly agriculture, to maximize their potential, it is crucial to identify emerging priority areas and existing research gaps for future research agendas. As a contribution to this effort, this paper employs the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology to review the state-of-the-art in the field of climate services for disaster risk management. A comprehensive search across five literature databases combined with a snowball search method using ResearchRabbit was conducted and yielded 242 peer-reviewed articles, book sections, and reports over 2013-2023 after the screening process. The analysis revealed flood, drought, and food insecurity as major climate-related disasters addressed in the reviewed literature. Major climate services addressed included early warning systems, (sub)seasonal forecasts and impact-based warnings. Grounded in the policy processes’ theoretical perspective, the main focus identified and discussed three prevailing policy-oriented priority areas: 1) development of climate services, 2) use-adoption-uptake, and 3) evaluation of climate services. In response to the limitations of the prevalent supply-driven and top-down approach to climate services promotion, co-production emerges as a cross-cutting critical aspect of the identified priority areas. Despite the extensive research in the field, more attention is needed, particularly pronounced in the science-policy interface perspective, which in practice bridges scientific knowledge and policy decisions for effective policy processes. This perspective offers a valuable analytical lens as an entry point for further investigation. Hence, future research agendas would generate insightful evidence by scrutinizing this critical aspect given its importance to institutions and climate services capacity, to better understand intricate facets of the development and the integration of climate services into disaster risk management.

Runout prediction of potential landslides based on the multi-source data collaboration analysis on historical cases

Long runout landslides involve a massive amount of energy and can be extremely hazardous owing to their long movement distance,high mobility and strong destructive power.Numerical methods have been widely used to predict the landslide runout but a fundamental problem remained is how to determine the reliable numerical parameters.This study proposes a framework to predict the runout of potential landslides through multi-source data collaboration and numerical analysis of historical landslide events.Specifically,for the historical landslide cases,the landslide-induced seismic signal,geophysical surveys,and possible in-situ drone/phone videos(multi-source data collaboration)can validate the numerical results in terms of landslide dynamics and deposit features and help calibrate the numerical(rheological)parameters.Subsequently,the calibrated numerical parameters can be used to numerically predict the runout of potential landslides in the region with a similar geological setting to the recorded events.Application of the runout prediction approach to the 2020 Jiashanying landslide in Guizhou,China gives reasonable results in comparison to the field observations.The numerical parameters are determined from the multi-source data collaboration analysis of a historical case in the region(2019 Shuicheng landslide).The proposed framework for landslide runout prediction can be of great utility for landslide risk assessment and disaster reduction in mountainous regions worldwide.

Study on Temperature Disaster Indicators of Passion Fruit in Southwest Fujian, China

In China, meteorological forecasting relies on meteorological data obtained from regional and national stations. However, there were discrepancies between the data collected from the meteorological station at the passion fruit growing base and the data from regional and national stations. Consequently, the high and low temperature disaster indicators determined by the meteorological station at the passion fruit growing base cannot be applied to meteorological forecasting. To address this issue and facilitate the monitoring and early warning of high and low temperature disasters in passion fruit cultivation in Fujian, China, we used multi-source hourly temperature data (including the data from meteorological observation stations in passion fruit growing bases, the nearest regional stations, and national surface conventional meteorological observation stations) in three cities in southwestern Fujian (Longyan, Sanming, and Zhangzhou) spanning the years 2020 to 2022. By employing comprehensive statistical analysis methods (0.5 interval division and Cumulative frequency), we identified that passion fruit in southwestern Fujian was susceptible to high temperature disasters during the blooming-fruiting period, as well as low temperature disasters during the sprouting period. Consequently, we developed high and low temperature disaster indicators based on data from regional and national stations for different phenological periods of passion fruit in this region.

Citizens’Mental Health Issues and Psychological Trauma Experience due to a Crowd-Crush Disaster in Korea

This study evaluated the state of anxiety, depression, post-traumatic stress disorder, general mental health, and mental well-beingamong citizens after a crowd-crush disaster in Korea. Individuals who experienced the crowd crush had significantly higheranxiety, depression, and post-traumatic stress disorder (PTSD) scores than those who did not (p < 0.001). Additionally,people who avoided the disaster area had significantly higher depression and PTSD scores than those who did not avoid thearea (p < 0.001). Those who directly witnessed the Seoul Halloween crowd crush had a significant difference in PTSD levels ineither group than those who experienced it indirectly (p = 0.005). There was a significant difference in PTSD scores in cases ofdirect damage or death of an acquaintance (p < 0.001). The Seoul Halloween crowd crush caused psychological damagethrough indiscriminate exposure to the public, and symptoms of PTSD appeared over a long period. It is crucial to provideessential resources for ongoing treatment and case management.

Disaster Risk Reduction (DRR) Education from Social Disciplinary Theoretical Perspectives

Disaster is a social phenomenon. The occurrence and impacts of disasters including the education sector can be studied through a social problem lens. This paper draws meaning and understanding of DRR education using the sociological disciplinary framework in a detailed qualitative case study of three schools as they responded to the devastating Gorakha earthquake in 2015 and other disasters in Nepal. This paper considers the three sub-disciplines of sociology: the sociology of disaster, the sociology of education and the sociology of education governance in a development context. These sub-disciplines are nested together to analyse social, political and historical factors and their relationships which are helpful to identify risks and vulnerabilities in the education sector in Nepal. These are the major areas to explore the disaster context and needs of context-specific education acts (hereafter DRR education) to minimise the potential risks of disasters. The article concludes that the social disciplinary framework is significantly useful to analyse DRR education provisions and implications of education governance to mobilise school in disaster preparedness, response and recovery.

Strategy for Protection of Cultural Heritage Exposed to the Natural and Man-Made Activity Disasters in Serbia

Natural disasters and the adverse human activities are the key events in the history of mankind that form our history and shape our collective memory to this day.People on the planet Earth are not obsessed only with natural hazards,caused by earthquakes,floods and volcanic eruptions,and troubles unlikely come solely from the action of nature.Disasters threatening the human race can be caused also by people themselves.Both types of disasters cause vast human suffering,at the same time destroying cultural heritage as well,that has the function of determining the identity of social communities.These sufferings should be added to those that can be determined only by in-depth analyses which are derived from the synergy of natural forces and mistaken choices made by the humans,when it comes to their habitat.The proposed strategic plan for protection of built heritage in emergency situations may become the powerful catalyst for the process of revitalization by which the social tissue of community is maintained and restored,creating the symbol of resistance by which it endures each and every natural element and evil men behaviour.

Analyzing Disaster Mitigation Policies in Japan and China from an Eco-DRR Perspective:A Comparative Study of Shanghai and Tokyo

Global climate change-induced natural disasters require international efforts and the adoption of Eco-Disaster Risk Reduction(Eco-DRR)policies for sustainable development.This study examines the status of Eco-DRR in disaster prevention policies through word cloud analysis,providing insights for policymakers to enhance disaster prevention amid increasing climate-related challenges.Tokyo and Japan prioritize soft aspects,while Shanghai and China emphasize engineering for flood prevention,revealing a gap in Eco-DRR application.Despite these differing approaches,the shared focus on water-related disasters indicates a shift towards urban resilience.Future research should assess policy effectiveness and the impact of Eco-DRR on disaster risk reduction and ecosystem protection.

Beyond Defense: Proactive Approaches to Disaster Recovery and Threat Intelligence in Modern Enterprises

As cyber threats keep changing and business environments adapt, a comprehensive approach to disaster recovery involves more than just defensive measures. This research delves deep into the strategies required to respond to threats and anticipate and mitigate them proactively. Beginning with understanding the critical need for a layered defense and the intricacies of the attacker’s journey, the research offers insights into specialized defense techniques, emphasizing the importance of timely and strategic responses during incidents. Risk management is brought to the forefront, underscoring businesses’ need to adopt mature risk assessment practices and understand the potential risk impact areas. Additionally, the value of threat intelligence is explored, shedding light on the importance of active engagement within sharing communities and the vigilant observation of adversary motivations. “Beyond Defense: Proactive Approaches to Disaster Recovery and Threat Intelligence in Modern Enterprises” is a comprehensive guide for organizations aiming to fortify their cybersecurity posture, marrying best practices in proactive and reactive measures in the ever-challenging digital realm.

Multi-source Data-driven Identification of Urban Functional Areas:A Case of Shenyang,China

Urban functional area(UFA)is a core scientific issue affecting urban sustainability.The current knowledge gap is mainly reflected in the lack of multi-scale quantitative interpretation methods from the perspective of human-land interaction.In this paper,based on multi-source big data include 250 m×250 m resolution cell phone data,1.81×105 Points of Interest(POI)data and administrative boundary data,we built a UFA identification method and demonstrated empirically in Shenyang City,China.We argue that the method we built can effectively identify multi-scale multi-type UFAs based on human activity and further reveal the spatial correlation between urban facilities and human activity.The empirical study suggests that the employment functional zones in Shenyang City are more concentrated in central cities than other single functional zones.There are more mix functional areas in the central city areas,while the planned industrial new cities need to develop comprehensive functions in Shenyang.UFAs have scale effects and human-land interaction patterns.We suggest that city decision makers should apply multi-sources big data to measure urban functional service in a more refined manner from a supply-demand perspective.

Characteristics and dynamic analysis of the February 2021 long-runout disaster chain triggered by massive rock and ice avalanche at Chamoli, Indian Himalaya

A massive rock and ice avalanche occurred on the western slope of the Ronti Gad valley in the northern part of Chamoli,Indian Himalaya,on 7 February 7,2021.The avalanche on the high mountain slope at an elevation of 5600 m above sea level triggered a long runout disaster chain,including rock mass avalanche,debris avalanche,and flood.The disaster chain had a horizontal travel distance of larger than 17,600 m and an elevation difference of 4300 m.In this study,the disaster characteristics and dynamic process were analyzed by multitemporal satellite imagery.The results show that the massive rock and ice avalanche was caused by four large expanding discontinuity planes.The disaster chain was divided into five zones by satellite images and field observation,including source zone,transition zone,dynamic entrainment zone,flow deposition zone,and flood zone.The entrainment effect and melting water were recognized as the main causes of the long-runout distance.Based on the seismic wave records and field videos,the time progress of the disaster was analyzed and the velocity of frontal debris at different stages was calculated.The total analyzed disaster duration was 1247 s,and the frontal debris velocity colliding with the second hydropower station was approximately 23 m/s.This study also carried out the numerical simulation of the disaster by rapid mass movement simulation(RAMMS).The numerical results reproduced the dynamic process of the debris avalanche,and the mechanism of long-runout avalanche was further verified by parametric study.Furthermore,this study discussed the potential causes of disaster and flood and the roles of satellite images and seismic networks in the monitoring and early-warning.

Multi-Source Data Privacy Protection Method Based on Homomorphic Encryption and Blockchain

Multi-Source data plays an important role in the evolution of media convergence.Its fusion processing enables the further mining of data and utilization of data value and broadens the path for the sharing and dissemination of media data.However,it also faces serious problems in terms of protecting user and data privacy.Many privacy protectionmethods have been proposed to solve the problemof privacy leakage during the process of data sharing,but they suffer fromtwo flaws:1)the lack of algorithmic frameworks for specific scenarios such as dynamic datasets in the media domain;2)the inability to solve the problem of the high computational complexity of ciphertext in multi-source data privacy protection,resulting in long encryption and decryption times.In this paper,we propose a multi-source data privacy protection method based on homomorphic encryption and blockchain technology,which solves the privacy protection problem ofmulti-source heterogeneous data in the dissemination ofmedia and reduces ciphertext processing time.We deployed the proposedmethod on theHyperledger platformfor testing and compared it with the privacy protection schemes based on k-anonymity and differential privacy.The experimental results showthat the key generation,encryption,and decryption times of the proposedmethod are lower than those in data privacy protection methods based on k-anonymity technology and differential privacy technology.This significantly reduces the processing time ofmulti-source data,which gives it potential for use in many applications.