Disaster Management Research Articles

An integrated framework for flood disaster information extraction and analysis leveraging social media data: A case study of the Shouguang flood in China

Flood disasters cause significant casualties and economic losses annually worldwide. During disasters, accurate and timely information is crucial for disaster management. However, remote sensing cannot balance temporal and spatial resolution, and the coverage of specialized equipment is limited, making continuous monitoring challenging. Real-time disaster-related information shared by social media users offers new possibilities for monitoring. We propose a framework for extracting and analyzing flood information from social media, validated through the 2018 Shouguang flood in China. This framework innovatively combines deep learning techniques and regular expression matching techniques to automatically extract key flood-related information from Weibo textual data, such as problems, floodings, needs, rescues, and measures, achieving an accuracy of 83 %, surpassing traditional models like the Biterm Topic Model (BTM). In the spatiotemporal analysis of the disaster, our research identifies critical time points during the disaster through quantitative analysis of the information and explores the spatial distribution of calls for help using Kernel Density Estimation (KDE), followed by identifying the core affected areas using the Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) algorithm. For semantic analysis, we adopt the Latent Dirichlet Allocation (LDA) algorithm to perform topic modeling on Weibo texts from different regions, identifying the types of disasters affecting each township. Additionally, through correlation analysis, we investigate the relationship between disaster rescue requests and response measures to evaluate the adequacy of flood response measures in each township. The research results demonstrate that this analytical framework can accurately extract disaster information, precisely identify critical time points in flood disasters, locate core affected areas, uncover primary regional issues, and further validate the sufficiency of response measures, therefore enhancing the efficiency in collecting disaster information and analytical capabilities.

Determining factors influencing flood preparedness among citizens in Jakarta: A protection motivation theory approach

The Jakarta Metropolitan Area is one of the cities heavily affected by floods in Asia. Effective disaster risk reduction strategies require deep understanding of what motivates its citizens towards preparedness and mitigation actions. This study aimed to discern these motivating factors among Jakarta's residents through the Protection Motivation Theory (PMT) approach. Through an online survey, 304 participants provided insights into how the extended PMT factors shape their Protection Motivation (PM) towards flood preparedness. A total of nine (9) variables and 41 constructs were considered in this study. PMT factors were comprised of PM, Perceived Severity (PS), Perceived Vulnerability (PV), Response Efficacy (RE), and Perceived Self-Efficacy (SE) along with additional factors such as Media (M), Government Preventive Actions (GA), Flood Experience (FE), and Geographical Perspectives (GP). Partial Least Square Structural Equation Modeling (PLS-SEM) revealed that SE had the most substantial impact on influencing PM (β = 0.522) followed by PV, PS, and RE. The results also showed the significance of M and GA to both SE and RE. Meanwhile, FE illustrates positive impact to both PV and PS. Interestingly, GP was found to be only significant to PV and not to PS. External factors M and GA demonstrated a significant indirect effect on PM through SE, whereas GP exhibited a significant indirect effect on PM through PV. These findings provide actionable insights for policymakers and disaster management practitioners, emphasizing the power of media and the need for comprehensive strategies that address both the psychological and practical aspects of flood disaster readiness in Jakarta and potentially other flood-prone regions.

Predicting forest fire probability in Similipal Biosphere Reserve (India) using Sentinel-2 MSI data and machine learning

The global escalation in forest fires, characterized by increasing frequency and severity, results from a complex interplay of natural and anthropogenic factors, exacerbated by climate change. These fires devastate habitats, threaten species, reduce biodiversity, disrupt natural cycles, and harm local ecosystems. The impacts are particularly damaging in biological reserves. The Similipal Biosphere Reserve (SBR) in Odisha State is one of India’s major forest fire hotspots, experiencing forest fires almost every year. The objective of this study is to develop a predictive model using Sentinel-2 MSI data and machine learning (ML) techniques to estimate the probability of forest fires in the SBR, India, thereby enhancing disaster management and prevention in the region. This research maps and quantifies forest fire intensity by leveraging ML algorithms, namely Extreme Gradient Boosting (XGBoost), Gradient Boosting Machine (GBM), Support Vector Machine (SVM), and Random Forest (RF). To develop a Forest Fire Probability (FFP) map, twenty conditioning factors, along with pre- and post-fire Normalized Burn Ratio (NBR) and delta Normalized Burn Ratio (dNBR), were utilized. Furthermore, four statistical methods—Mean Absolute Error, Mean Square Error, Root Mean Square Error, and Overall Accuracy—were employed to analyze the FFP. The results were validated using the Area Under Curve (AUC) method. The analysis identifies 2021 as the year with the highest incidence of forest fires, accounting for 29.19% of the occurrences. Among the models, the GBM exhibits superior performance, highlighting its efficacy in handling large, multidimensional datasets. Predictive mapping suggests that approximately 1400–1500 km2, or 25–30% of the studied area, faces a high to very high risk of forest fires.

Requirements Engineering for a Drone-Enabled Integrated Humanitarian Logistics Platform

The pursuit of ameliorating humanitarian logistics (HL) through the integration of cutting-edge technologies has received significant attention in recent years. AIRDROP is a visionary platform conceived to offer a cohesive disaster management approach spanning from preparedness to recovery of a wide range of natural and human-made disasters. AIRDROP aims to be a scalable, modular and flexible solution, employing an array of drones of different sizes and payload capabilities, able to provide different HL services to first responders and operational decision-makers. This study aims to elicit, specify and validate the requirements for AIRDROP to ensure their applicability across a broad spectrum of disaster scenarios and the entire disaster management continuum. This research utilized a thorough literature review and expert consultations to systematically elicit and specify the AIRDROP requirements, ensuring they were grounded in both academic foundations and practical industry standards. The validation process involved a questionnaire survey administered to 26 participants from various professional backgrounds. The requirements were prioritized using the MoSCoW methodology, and significant differences among participant groups were identified through the Kruskal–Wallis H and Mann–Whitney U tests. Furthermore, two critical requirements emerged from open-ended responses. As a result, 276 out of the initially defined 335 requirements in total advanced to the design phase. It is worth noting that the dynamic nature of requirements in HL necessitates ongoing assessment and adaptation to keep AIRDROP at the forefront and aligned with evolving needs.

Urban planning approaches to support community-based flood adaptation in North Jakarta Kampungs

PurposeThe ineffectiveness of flood control in climate-impacted majority-world cities like Jakarta highlights the need for policies that integrate local knowledge and embrace water harmony rather than resistance. This study explores flood adaptation in North Jakarta's kampungs (urban informal settlements), aiming to enhance the efficacy of current flood disaster management. The outcomes of the participatory planning mechanism simulation that we propose are expected to provide valuable insights for the urban planning approach in that city.Design/methodology/approachWe employed focus groups and design charrettes with a bottom-up approach to explore how local knowledge can enhance spatial flood management and urban planning policies. In total, 17 diverse participants, covering various ages and professions, engaged in these activities. Our methods aimed to be culturally sensitive and inclusive, embracing indigenous values like musyawarah and gotong royong. The research methodically examined flood implications and adaptations in informal settlements, progressing through preliminary understanding, data triangulation, and a reflective synthesis of the findings.FindingsAmid worsening global changes like sea level rise, community-focussed, collaborative planning can help create tailored flood-resilience solutions. The research reveals that partnerships between communities and organisations promote city-wide, flood-adapted environments, aligning policy with the needs and goals of those most affected by flooding. This collaboration enhances flood disaster management and planning policies.Research limitations/implicationsThis research focusses on Jakarta's flood adaptation and urban planning, reflecting on historical situations relevant to urbanising majority-world countries. Whilst specific to Jakarta, it offers perspectives on managing global environmental challenges such as sea level rise. Subsequent research should prudently consider each locale's distinct geographic and social milieu and the trust in planning systems in applying these findings, methodologies and approaches.Originality/valueThis study clarifies the relationship between disaster management, policy and flood adaptation, focussing on local knowledge in North Jakarta, pertinent to urbanising majority-world nations. Jakarta's historical and modern dynamics, including globalisation, reveal specific prospects and obstacles to applying vernacular knowledge to planning and disaster response. It highlights crucial points for policymakers in the majority of the world to address growing flood risks and create strategies that integrate local and traditional wisdom.

A glacial lake outburst flood risk assessment for the Phochhu river basin, Bhutan

Abstract. The melting of glaciers has led to an unprecedented increase in the number and size of glacial lakes, particularly in the Himalayan region. A glacial lake outburst flood (GLOF) is a natural hazard in which water from a glacial or glacier-fed lake is swiftly discharged. GLOFs can significantly harm life, infrastructure, and settlements located downstream and can have considerable ecological, economic, and social impacts. Based on a dam breach model, BREACH, and a hydrodynamic model, HEC-RAS (Hydrologic Engineering Centre's River Analysis System), we examined the potential consequences of a GLOF originating from the Thorthomi glacial lake, located within the Phochhu river basin, one of Bhutan's largest and rapidly expanding glacial lakes. Our analysis revealed that following a breach the Thorthomi glacial lake will likely discharge a peak flow of 16 360 m3 s−1 within 4 h. Such a discharge could potentially cause considerable damage, with an estimated 245 ha of agricultural land and over 1277 buildings at risk of inundation. To mitigate ecological, economic, and social impacts on downstream areas, our results emphasise an urgent need for understanding and preparing for the potential consequences of a GLOF from Thorthomi lake. Our findings provide valuable insights for policymakers and stakeholders involved in disaster management and preparedness.

The disaster literacy level of society, opinions on disaster management services, and related factors: A case from a province in Turkiye.

This study aimed to investigate individuals' disaster literacy levels, their thoughts on disaster management services, and related factors. The descriptive research was conducted with a total of 480 individuals between the ages of 18 and 60 living in a province in the first-degree earthquake zone in Turkey. The data were collected with the Disaster Literacy Scale and the Disaster Management Services Evaluation Form. In this study, it was found that more than half of the participants had inadequate/moderate disaster literacy. The total disaster literacy score and all subscale scores were found to be higher in individuals who had an earthquake kit at home, had an emergency plan, and had received disaster-related training (p<.05). The majority of the participants found the services provided for disaster management inadequate or very inadequate. In this study, it has been found that a significant portion of the society lacks the desired level of disaster literacy, and the services provided for disasters are deemed insufficient therefore, there is a need for multi-disciplinary national prevention programs to enhance disaster literacy and preparedness in the community, as well as urgently supporting policies to improve disaster-related services. Additionally, due to their comprehensive knowledge of the general structure of society, public health nurses need to play an active role in disaster preparedness and increasing disaster literacy levels.

Nursing leadership in disaster preparedness and response lessons learned and future directions

Nursing leadership is integral to effective disaster preparedness and response, demanding agility, strategic planning, and robust resource management. Disasters, both natural and man-made, present unique challenges that require nursing leaders to be adept in rapid decision-making and efficient coordination. The unpredictability of disasters necessitates comprehensive and regularly updated preparedness plans, ensuring nursing teams are well-versed in their roles through continuous training and simulations. Interprofessional collaboration and strong community partnerships are essential, fostering a unified response and efficient resource distribution. Communication remains a cornerstone of disaster management, with clear, timely information dissemination being crucial for coordinating efforts and maintaining public trust. Resource scarcity during disasters, such as shortages of medications and personal protective equipment (PPE), poses significant challenges. Effective resource management strategies are essential to maintain patient care standards and protect healthcare workers. The psychological toll on healthcare providers is another critical issue, with nursing leaders needing to implement support systems that address mental health and promote resilience. Integrating advanced technologies like telehealth and data analytics can enhance disaster response, providing remote care options and enabling predictive modeling for better resource allocation. Innovative approaches in nursing leadership include the use of telehealth for remote consultations and monitoring, data analytics for predictive modeling, and interprofessional education to improve teamwork and coordination. Strengthening community partnerships enhances overall disaster response capacity, ensuring that community-specific needs are addressed. Mental health support for healthcare providers, both immediate and long-term, is vital for maintaining a resilient workforce. Policy advocacy by nursing leaders is crucial to developing robust disaster preparedness frameworks and securing adequate funding. In summary, nursing leadership is pivotal in disaster preparedness and response, with a focus on strategic planning, interprofessional collaboration, effective communication, resource management, mental health support, and the integration of advanced technologies. Emphasizing these areas will enhance the resilience and responsiveness of healthcare systems, ultimately improving outcomes for patients and healthcare providers during disasters.

A multicriteria decision model to improve emergency preparedness: Locating-allocating urban shelters against floods

The increasing prominence of natural disasters in recent years has made emergency disaster management one of the main challenges for building resilient societies. This way, the warming combination with human-induced urban interventions with climate change reshapes the strategic decision-making that public managers must deal with to promote safety and preparedness. Consequently, it implies complex situations under uncertainty that comprise conflicting dilemmas about which decision-makers (DMs) should establish trade-offs. Given this backdrop, this work proposes a risk-based multidimensional model for assessing urban shelter location against flood risk with Decision Analysis and Multi-Attribute Utility Theory. By using principles of the Queue Theory, the decision model undertakes four criteria that measure in a probabilistic manner the alternatives’ performance when considering urban flood disaster evacuation routes in affected areas. Altogether, it enables the DM to establish his/her preferences with views to rank potential locations for mounting temporary shelters and for planning emergency preparedness in risky areas. Being replicable to any urban context, our proposal has the potential to support and promote the development and implementation of crisis protocols consisting of measures to reduce flood impacts and adapt critical areas to climate change. From a numerical application of the proposed model, the DM is encouraged to explore graphical and tabular visualization tools, as well as sensitivity analysis, to establish in strategic terms how to design and implement emergency shelters to reduce flood risks in urban areas effectively.

Geospatial Data Integration for the Flood Vulnerable Area Classification in Jratunseluna Watershed

Flood is a threat that has significant impacts on communities and the environment. To improve the management of disaster risk, this research takes an integrated approach by utilizing geospatial data from various sources. The main objective of this research is to provide an integrated approach to determining flood-vulnerable area classes. This research focuses on the processing of various geospatial data such as DEM (Digital Elevation Model) imagery, Landsat 8 satellite imagery, Hydrological data based on SHuttle Elevation Derivatives at multiple Scales (HydroSHEDS) water flow accumulation imagery, and Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) rainfall imagery which are used as data sources to model the flood vulnerable area classification of The Jratunseluna watershed. Landsat 8 satellite imagery is used as a source for landuse land cover (LULC) classification, it is done to score each land category to the level of ability to absorb and drain excess water, the remaining data is used to score the earth elevation, accumulated water flow, and rainfall from the area. The weights and scores are used as the basis values to create a flood-vulnerable area classification model. The result of this research is a flood-vulnerable area classification map generated from a pre-made model.

A novel multi-model ensemble framework for fluvial flood inundation mapping

Floods pose a significant threat to communities and infrastructure, necessitating timely predictions for effective disaster management. Conventional hydrodynamic models often encounter limitations in data requirements and computational efficiency. To overcome these constraints, we propose a novel multi-model ensemble framework integrating the flood extent and depth models for fluvial flood mapping. Various flood conditioning factors, such as terrain elevation and slope, flow direction, distance from the river, and latitude-longitude, were selected as model inputs, considering their relevance. The proposed framework was evaluated for predictive, extrapolative, and generalization capabilities. Results indicate that the proposed model successfully captures flood dynamics across a wide range of streamflow values, including unforeseen events, making it a valuable tool for predicting flood extent and depth. Overall, our approach offers a promising alternative to conventional hydrodynamic models, providing robustness, computational efficiency, scalability, automation, and integration with existing tools for flood inundation mapping tasks.

Unveiling the heart of disaster nursing: A qualitative study on motivations, challenges, and lessons from the devastating 2023 Turkey earthquakes.

Turkey has faced a notable escalation in earthquake disasters in the last two decades. Despite initiating a health and disaster management system, nurses' pivotal roles and experiences in handling such crises have been disregarded. This qualitative study analyzed nurses' experiences before, during, and after deployment in response to the 2023 Turkey earthquakes to enhance disaster-response efforts. This descriptive qualitative study was conducted between March and May 2023 using semistructured interviews with 15 nurses purposively sampled among those who volunteered to work in the earthquake zone. The MIRACLE and COREQ guidelines were followed for reporting qualitative research. The analysis exposed five main themes for pre-tasking: moral obligation, motivation, insufficient experience, balancing responsibilities, and preparation challenges. The peri-task themes include responsibilities, skills, bravery and characteristics, workload management, teamwork, and outcomes. Post-tasking has three themes: competence assessment, career goals and aspirations, and support. Training and coping with anxiety and stress are common themes for all phases. Disaster relief requires a comprehensive and coordinated response from healthcare organizations, government agencies, and support systems. Providing adequate training, ensuring safety protocols, offering mental health support, and fostering a fair and supportive work environment are crucial steps in mitigating the adverse effects on nurses and, by extension, the patient care process in earthquake-affected areas. Nurse training in disaster preparedness should cover various response methods and involve multiple disciplines. Managers can help by arranging drills, simulations, online courses, and workshops and promoting partnerships for improved collaboration. Psychological support should be included to address emotional challenges. Regularly updating response policies based on past experiences is crucial for preparedness and efficiency.

Stormy sales: the influence of weather expectations on FMCG consumption

PurposeThis study aims to understand the impact of extreme weather events on fast-moving consumer goods (FMCG) consumption and to examine the role of anticipated product scarcity and FMCG types on such behavior.Design/methodology/approachThis paper conducted five studies, combining archival data analysis with behavioral experiments. The archival data included sales data from a supermarket chain and weather data from the National Weather Service. The experiments were designed to test the effect of extreme weather cues on consumption, the psychological mechanism behind this effect and moderators.FindingsThis research found that consumers’ anticipation of extreme weather events significantly increases their consumption of FMCGs. This research further discovered that these behaviors are driven by anticipated product scarcity and moderated by consumers’ altruisms and FMCG types.Research limitations/implicationsLimitations of the research include the reliance on reported sales data and self-reported measures, which could introduce biases. The authors also primarily focused on extreme weather events, leaving other types of disasters unexplored. Furthermore, cultural differences in disaster response might influence results, yet the studies do not fully address these nuances. Despite these limitations, the findings provide critical insights for FMCG retailers and policymakers, suggesting strategies for managing demand surges during disasters. Moreover, understanding consumer behavior under impending disasters could inform intervention strategies, potentially mitigating panic buying and helping ensure equitable resource distribution. Last, these findings encourage further exploration of environmental influences on consumer behavior.Practical implicationsThe findings have practical implications for products, brand managers and retailers in managing stock levels and product distribution during disasters. Furthermore, understanding the psychological mechanisms of these behaviors could inform policymakers’ designs of public interventions for equitable resource allocation during extreme weather events.Social implicationsThe research provides significant social implications by highlighting how extreme weather events impact FMCG consumption. This understanding can guide public policymakers in creating efficient disaster management plans. Specifically, anticipating surges in FMCG purchases can inform policies for maintaining price stability and preventing resource shortages, mitigating societal stress during crises. Moreover, these findings encourage public education around responsible purchasing during disasters, potentially reducing panic buying. By collaborating with FMCG manufacturers and retailers, governments can ensure a steady supply of essentials during extreme weather events. Thus, the research can play a crucial role in enhancing societal resilience in the face of impending disasters.Originality/valueTo the best of the authors’ knowledge, this is the first study to integrate the impact of extreme weather events on consumption behavior with the psychological theory of anticipated product scarcity. The unique focus on FMCGs offers a novel perspective on consumer behavior literature.

Assessment of urban inundation and prediction of combined flood disaster in the middle reaches of Yellow river basin under extreme precipitation

Watershed Cities often face the dual flood risks of upstream floods and urban waterlogging. This compound disaster is caused by the interaction of multiple factors (topography, storms, upstream water and urban drainage, etc.), and is particularly affected by extreme rainfall events and the large and small drainage carrying capacity. To explore the combined effects of extreme rainfall and drainage systems, this study applied the Chicago rainfall hyetograph method and constructed design under different return periods. A comprehensive model combining the verified SWMM and LISFLOOD hydraulic models was utilised to build one-dimensional and two-dimensional coupled watershed urban flood models, simulating inundation depth and duration. By considering urban rainfall, runoff, topography, and drainage system characteristics, the flood processes under different extreme rainfall scenarios were simulated, and the node overflow and inundation depth were obtained. A flood risk assessment of watershed cities was conducted by combining methods such as the HR threshold method, evaluation level, and threshold division. In this study, Huyi District of Xi’an City, China, was used as the research area. The main research findings show: (1) the precipitation design values for different return periods were calculated based on the proposed method, with amount for a 100-year event being 87.196 mm, suitable for composite flood simulation; (2) applying the SWMM-LISFLOOD coupled model constructed for midstream urban areas ensured the accurate simulation of composite flood processes. Under different extreme rainfall return period scenarios, the percentage of flood inundation depth exceeding 1 m caused by multiple disaster factors increased from 0.73 % to 5.19 %; (3) extreme rainfall leads to different levels of internal flooding risk. When the return period of extreme rainfall is 5 years, the area inundated by composite floods in the research area is 67.21 km2. In comparison, when the return period of extreme rainfall is 50 years, the area inundated by composite floods in the research area significantly increases to a total inundation area of 286.85 km2. This study is expected to provide scientific references for flood prevention planning, composite disaster risk management, and urban resilience building in watershed urban areas while significantly impacting modern economic growth, social stability, and the safety of watershed cities.

FLOOD SUSCEPTIBILITY MAPPING USING MACHINE LEARNING IN KENING RIVER, SUB WATERSHED OF BENGAWAN SOLO, TUBAN

Floods are natural occurrences with the potential to cause damage to ecosystems and pose significant threats to human life, resulting in the destruction of property, infrastructure, and socioeconomic challenges. In recent times, flooding in the Sub-Watershed of Bengawan Solo has been linked to the overflowing Kening River in Tuban County. Aim: This study aims to produce a flood susceptibility map to mitigate the frequency of flood occurrences as well as facilitate effective planning for flood disaster risk management. Methodology and results: Flood data is collected from 2016 to 2023 through field surveys, Sentinel-1 satellite imagery, and data from the Development Planning Agency, Tuban County. Integrating remote sensing data from satellite imagery (PlanetScope, Sentinel-2), geographic information systems (GIS), and spatial modeling techniques, a flood susceptibility map is developed for the Kening River catchment. The occurrence of floods in the Kening River area is associated with various factors (11 variables) assessed through the frequency ratio approach, including profil curvature, LS factor, aspect, rainfall, river distance, road distance, building density 100 m, road density 100 m, vegetation type, normalized difference water index (NDWI), and soil adjusted vegetation index (SAVI). The results show flood susceptibility maps utilizing frequency ratio (FR) and convolutional neural network (CNN) techniques. The flood susceptibility map obtained through the CNN method demonstrates a notably high AUC value. The model development generated a validation AUC value of 0.857 for training and 0.856 for testing. Conclusion, significance and impact study: This research provides an understanding into the factors that influence the occurrence of floods in the Kening River catchment area. It also emphasizes the benefit of advanced machine learning approaches in mapping the susceptibility of floods. Furthermore, this study has the potential to be helpful in guiding regional policy decisions and result in enhanced flood risk management measures in Tuban County.

Mission-critical UAV swarm coordination and cooperative positioning using an integrated ROS-LoRa-based communications architecture

Over the recent years unmanned aerial vehicles (UAVs) have been utilized extensively in mission-critical operations, especially as it relates to disaster management scenarios. Clearly, during these missions, the UAVs should be able to communicate effectively (amongst themselves and with the ground control station (GCS)) in order to transmit and receive commands and other related information. Moreover, accurate positioning is paramount during this type of operations. This has motivated the exploration of a number of alternative navigation methods to address robustness issues that arise when the global positioning system (GPS) becomes unavailable, due to GNSS disruption or sensor malfunction. This work addresses these issues by initially developing and implementing an integrated LoRa-ROS-based (long range communication - robot operating system) system for UAV-to-X communications. Subsequently, it presents a novel cooperative positioning approach, where a group of autonomous UAVs employ various algorithms (detection, tracking, communication, and localization) for cooperative positioning in order to counter any GPS/sensor malfunction. For evaluation purposes, a prototype multi-agent system is designed and implemented, utilizing the proposed integrated ROS-LoRa-based communication architecture, as well as sensor (inertial measurement unit - IMU) fusion. Specifically, LoRA mesh networking (using a custom printed circuit board - BALORA), is incorporated to maintain communication and distribute the sensor information between the UAVs. The prototype of the proposed communications architecture and cooperative relative positioning system (CRPS) is subsequently tested in a real-world environment, demonstrating the feasibility and effectiveness of the proposed communications solution, as well as the robust and accurate localization that is analogous to the ground truth (GPS+IMU).

Multi-hazard life-cycle consequence analysis of deteriorating engineering systems

Probabilistic life-cycle consequence (LCCon) analysis (e.g., assessment of repair costs, downtime, or casualties over an asset’s service life) can enable optimal life-cycle management of critical assets under uncertainties. This can lead to effective risk-informed decision-making for future disaster management (i.e., risk mitigation and/or resilience-enhancing strategies/policies) implementation. Nevertheless, despite recent advances in understanding, modeling, and quantifying multiple-hazard (or multi-hazard) interactions, most available LCCon analytical formulations fail to accurately compute the exacerbated consequences which may stem from incomplete or absent repair actions between different interacting hazard events. This paper introduces a discrete-time, discrete-state Markovian framework for efficient multi-hazard LCCon analysis of deteriorating engineering systems (e.g., buildings, infrastructure components) that appropriately accounts for complex interactions between natural hazard events and their effects on a system’s performance. The Markovian assumption is used to model the probability of a system being in any performance level (i.e., limit state) after multiple hazard events inducing either instantaneous and/or gradual deterioration and after potential repair actions through implementing stochastic (transition) matrices. LCCon estimates are then obtained by combining limit state probabilties with suitable system-level consequence models in a computationally efficient manner. The proposed framework is illustrated for two case studies subject to earthquake and flood events as well as environment-induced corrosion during their service life. The first is a reinforced concrete building and the second is a simple transportation road network with a reinforced concrete bridge.

Early Detection and Stability Assessment of Hazardous Rock Masses in Steep Slopes

The assessment of slope stability plays a critical role in the prevention and management of slope disasters. Evaluating the condition and stability of hazardous rock masses is essential for predicting potential collapses and assessing treatment effectiveness. However, conventional measurement techniques are inadequate in high slope areas, which lack sufficient spatial data to support subsequent calculations and analyses. Therefore, this paper presents a method for the early identification and evaluation of unstable rock masses in high slopes using Unmanned Aerial Vehicle (UAV) digital photogrammetry and geographic information technology. By considering nine evaluation indices including geology, topography, and induced conditions within the study area, weights for each index are determined through an analytic hierarchy process. A semi-automatic approach is then utilized to extract and analyze rock mass stability. The reliability of this early identification method is confirmed by applying the limit equilibrium principle. The findings reveal that 17.6% of dangerous rock masses in the study area fall into the unstable category (W4, W6, W10). This method effectively assesses slope rock mass stability while providing technical support for disaster monitoring systems, warning mechanisms, and railway infrastructure safety defense capability to ensure safe mountain railway operations.

QUIC Congestion Control Algorithm characteristics in mixed satellite–terrestrial emergency communication scenarios

Reliable communications play a pivotal role in ensuring an efficient response and the coordination of recovery and rescue efforts. However, conventional communication methods may not always be accessible or dependable in such situations. In such circumstances, constellations of Low Earth Orbit (LEO) satellites can provide high bandwidth capabilities with relatively low latency, making them well-suited for supporting on-the-ground disaster management teams. Satellites can either complement or replace terrestrial telecommunication infrastructures. In this context, reliance on the recently defined QUIC protocol allows for a seamless transition from terrestrial to satellite communication as needed. Therefore, we investigate the possible use of a dual-stack node architecture along with the employment of the QUIC transport protocol for emergency communications, assuming that the backhaul link used to transfer users’ applications data may need to be changed (seamlessly). We conduct an extensive emulation study, evaluating the performance of QUIC under varying queuing policies and Congestion Control Algorithm (CCA) behaviour, providing practical insights and recommendations to enhance the protocol’s efficiency and robustness. The key aspects and configurations of QUIC protocol stack are identified, presenting optimal communication configurations leveraging CoDel and BBR CCA.

Seismic Magnitude Forecasting through Machine Learning Paradigms: A Confluence of Predictive Models

This study focuses largely on earthquake prediction, which is a crucial element of geoscience and emergency and disaster management. We apply state-of- the-art machine learning methods, most notably the Random Forest Regression approach, to examine the intricate link between geographical data analysis and earthquake prediction. Once we have patiently traversed the challenges of seismic data processing, we create prediction models that deliver insights via sophisticated visualization of earthquake occurrences. The research offers confirmation that machine learning approaches perform exceptionally well for forecasting earthquakes. These results show the relevance of these paradigms for enhancing, among other things, early warning systems and catastrophic preparedness measures.