Search Results (89)

Accurately monitoring terrestrial water storage (TWS) variations is essential due to global climate change and growing water demands. This study investigates TWS changes in Oregon, USA, using Global Navigation Satellite System (GNSS) data from the Nevada Geodetic Laboratory, Gravity Recovery and Climate Experiment (GRACE) level-3 mascon data from the Jet Propulsion Laboratory (JPL), and Noah model data from the Global Land Data Assimilation System (GLDAS) data. The results show that the GNSS inversion offers superior spatial resolution, clearly capturing a water storage gradient from 300 mm in the Cascades to 20 mm in the basin and accurately distinguishing between mountainous and basin areas. However, the GRACE data exhibit blurred spatial variability, with the equivalent water height amplitude ranging from approximately 100 mm to 145 mm across the study area, making it difficult to resolve terrestrial water storage gradients. Moreover, GLDAS exhibits limitations in mountainous regions. The GNSS can provide continuous dynamic monitoring, with results aligning well with seasonal trends seen in GRACE and GLDAS data, although with a 1–2 months phase lag compared to the precipitation data, reflecting hydrological complexity. Future work may incorporate geological constraints, region-specific elastic models, and regularization strategies to improve monitoring accuracy. This study demonstrates the strong potential of GNSS technology for monitoring TWS dynamics and supporting environmental assessment, disaster warning, and water resource management. Full article

In recent years, UAV (unmanned aerial vehicle)-UGV (unmanned ground vehicle) collaborative systems have played a crucial role in emergency disaster rescue. To improve rescue efficiency, heterogeneous network and task chain methods are introduced to cooperatively develop rescue sequences within a short time for collaborative systems. However, current methods also overlook resource overload for heterogeneous units and limit planning to a single task chain in cross-platform rescue scenarios, resulting in low robustness and limited flexibility. To this end, this paper proposes Gemini, a cascaded dual-agent deep reinforcement learning (DRL) framework based on the Heterogeneous Service Network (HSN) for multiple task chains planning in UAV-UGV collaboration. Specifically, this framework comprises a chain selection agent and a resource allocation agent: The chain selection agent plans paths for task chains, and the resource allocation agent distributes platform loads along generated paths. For each mission, a well-trained Gemini can not only allocate resources in load balancing but also plan multiple task chains simultaneously, which enhances the robustness in cross-platform rescue. Simulation results show that Gemini can increase rescue effectiveness by approximately 60% and improve load balancing by approximately 80%, compared to the baseline algorithm. Additionally, Gemini’s performance is stable and better than the baseline in various disaster scenarios, which verifies its generalization. Full article

Scenario analysis and the modeling of typhoons are fundamental prerequisites for effective emergency decision-making. However, current studies on typhoon scenario modeling lack analyses of cascading effects and users’ concerns, failing to represent cascading disaster impacts and user adaptability. This study constructs a scenario evolution model for typhoons and their cascading disasters through typhoon-related public opinion mining and an analysis of disaster evolution characteristics to address these limitations. Specifically, this study analyzes and extracts information about users’ sentiments and concerns based on public opinion data. Then, public opinion and typhoon evolution progression analyses are conducted, identifying cascading disaster evolution characteristics to determine scenario elements. The scenario model is constructed by calculating scenario node probability distributions using dynamic Bayesian networks (DBNs). In this study, Typhoon Bebinca is selected to verify the proposed scenario model; the results demonstrate that the model is reliable and its evolution process aligns with the impacts of typhoon cascading disasters. This study also reveals two critical insights: (1) Users’ concerns will change with typhoon evolution. (2) Emergency measures for dealing with typhoons and their cascading disasters are fragmented. It is essential to consider their cascading effects when enacting these measures. These findings provide novel insights that could aid government agencies in their decision making. Full article

The prevention and control of disasters in underground mines is a key task to ensure sustainable mining production and the development of society. The disaster chain brings cascading and clustering characteristics to disasters and leads to the expansion of their impacts and losses. It brings great difficulties to disaster prevention and control. This paper focuses on the disaster chain of a non-coal underground mine. It analyzes disaster events triggered by artificial mining activities based on a literature review, expert investigation, and field research. Subsequently, it constructs a complex network model of disaster chains containing 44 disaster nodes and 136 connecting edges. Then it performed a quantitative analysis of the complex network model, and studied complex network model parameters including degree, number of subnets, intermediate centrality, node importance, average path length, edge betweenness, connectivity, and edge vulnerability. On that basis, this paper reveals that the top five key nodes of the disaster chain are surface subsidence (H4), industrial site destruction (H7), well flooding (H21), equipment damage (H8), and living area damage (H11). It also reveals that the top five key edges of the disaster chain are mine water inrush (H6)→well flooding (H21), surface subsidence (H4)→industrial site destruction (H7), underground space failure (H3)→industrial site destruction (H7), gob collapse (H2)→surface subsidence (H4), and gob collapse (H2)→landslide (H5). Finally, this paper proposes specific chain-breaking disaster mitigation measures. Implementing these actions can play a pivotal role in mitigating the impact of mine disasters, preserving lives, and sustaining regional prosperity. Full article

Post-earthquake fires (PEFs) represent a complex, cascading hazard in which seismic damage creates ignition conditions that can overwhelm urban infrastructure and severely compromise structural integrity. Despite growing scholarly attention, the literature on PEFs remains fragmented across disciplines, lacking a consolidated understanding of structural vulnerabilities, urban-scale impacts, and response strategies. This study presents a systematic and thematic synthesis of 54 peer-reviewed articles, identified through a PRISMA-guided screening of 151 publications from the Web of Science Core Collection. By combining bibliometric mapping with thematic clustering, the review categorizes research into key methodological domains, including finite element modeling, experimental testing, probabilistic risk analysis, multi-hazard frameworks, urban simulation, and policy approaches. The findings reveal a dominant focus on structural fire resistance, particularly of seismically damaged concrete and steel systems, while highlighting emerging trends in sensor-based fire detection, AI integration, and urban resilience planning. However, critical research gaps persist in multi-hazard modeling, firefighting under partial collapse, behavioral responses, and the integration of spatial, infrastructural, and institutional factors. This study proposes an interdisciplinary research agenda that connects engineering, urban design, and disaster governance to inform adaptive, smart-city-based strategies for mitigating fire risks in seismic zones. This work contributes a comprehensive roadmap for advancing post-earthquake fire resilience in the built environment. Full article

International trade plays a pivotal role in shaping global supply chains, which are increasingly vulnerable to disruptions caused by geopolitical tensions, pandemics, and environmental disasters. These disturbances, particularly in maritime logistics, can trigger cascading effects across global industries. This study aims to identify and prioritize strategic responses to such disruptions by employing a combined qualitative exploratory approach and the Analytic Hierarchy Process (AHP). Expert judgments were obtained from 32 senior professionals across the maritime logistics and port management sectors during a structured evaluation conducted in the second quarter of 2025. AHP was utilized to systematically assess these inputs and determine the relative importance of resilience strategies. The results emphasize the need for adaptive, proactive, and sustainable logistics approaches to ensure long-term stability in maritime trade. By bridging a gap in the literature concerning integrated assessment of disruption responses, the study offers valuable insights for industry stakeholders and policymakers navigating an increasingly volatile global trade environment. Full article

Accurate flood forecasts are imperative to supervise and prepare for extreme events to assess the risks and develop proactive prevention strategies. The flood time-series data exhibit both spatial and temporal structures and make it challenging for the models to fully capture the embedded features due to their complex stochastic nature. This paper proposed a new approach for the first time using variational mode decomposition (VMD) hybridized with Gaussian process regression (GPR) to design the VMD-GPR model for daily flood forecasting. First, the VMD model decomposed the (t − 1) lag into several signals called intrinsic mode functions (IMFs). The VMD has the ability to improve noise robustness, better mode separation, reduced mode aliasing, and end effects. Then, the partial auto-correlation function (PACF) was applied to determine the significant lag (t − 1). Finally, the PACF-based decomposed IMFs were sent into the GPR to forecast the daily flood index at (t − 1) for Jeddah and Jazan stations in Saudi Arabia. The long short-term memory (LSTM) boosted regression tree (BRT) and cascaded forward neural network (CFNN) models were combined with VMD to compare along with the standalone versions. The proposed VMD-GPR outperformed the comparing model to forecast daily floods for both stations using a set of performance metrics. The VMD-GPR outperformed comparing models by achieving R = 0.9825, RMSE = 0.0745, MAE = 0.0088, E_NS = 0.9651, KGE = 0.9802, IA = 0.9911, U_95% = 0.2065 for Jeddah station, and R = 0.9891, RMSE = 0.0945, MAE = 0.0189, E_NS = 0.9781, KGE = 0.9849, IA = 0.9945, U_95% = 0.2621 for Jazan station. The proposed VMD-GPR method efficiently analyzes flood events to forecast in these two stations to facilitate flood forecasting for disaster mitigation and enable the efficient use of water resources. The VMD-GPR model can help policymakers in strategic planning flood management to undertake mandatory risk mitigation measures. Full article

In the case of extreme disasters such as local rainstorm and excessive flood, the safety risk analysis and prevention and control of cascade reservoirs face new challenges. Therefore, this article conducted a risk analysis based on typical watersheds and proposed a method for calculating the risk rate of overtopping in cascade reservoir groups, dynamically simulated the evolution process of overtopping floods in cascade reservoirs under different scenarios, delineated the scope of flood inundation, and evaluated the risk of overtopping of cascade reservoirs under different scenarios. Research has shown that dam failure floods in cascade reservoirs have both cumulative and cumulative effects, with scenario 3 being the most unfavorable. In scenario 3, the peak flow rates at the dam sites of each reservoir reached 24,500, 19,200, and 20,100 m³/s. According to the comprehensive risk assessment criteria, scenarios 1 and 2 are classified as moderate risks, while scenario 3 is classified as mild risk. Research has found that although the probability of dam overflow is extremely low, the high vulnerability calculated for each scenario indicates that a breach will cause significant social losses. This study can provide reference for the risk assessment of overtopping in cascade reservoirs and flood control and disaster reduction. Full article

Urban transportation systems, particularly integrated bus–metro networks, play a critical role in sustaining city functions but face significant vulnerability during extreme flood disasters. Taking Zhengzhou, China, as a case study, this study developed a comprehensive assessment model to evaluate the resilience of urban bus–metro hybrid networks under flood scenarios. First, a complex network-based bus–metro hybrid transportation network model was established, incorporating quantifiable flood disaster risk indices considering disaster-inducing factors, hazard-prone environments, and disaster-bearing entities. A cascading failure model was then constructed to simulate the propagation of node failures and passenger load redistribution during flood events. Subsequently, network resilience was evaluated using the topological metric of the relative size of the largest connected component and the functional metric of global efficiency. The analysis examined the influence of the load capacity sensitivity parameters α and β on resilience outcomes. Simulation results indicated that the parameter combination α = 0.8 and β = 2.0 yielded the highest resilience under the tested conditions, offering a balance between redundancy and the targeted protection of high-load nodes. Additionally, recovery strategies prioritizing nodes based on betweenness centrality significantly improved resilience outcomes. This study provides valuable insights and practical guidance for improving urban transportation resilience, assisting policymakers and planners in better mitigating flood disaster impacts. Full article

In response to the urgent need for rapid and precise post-disaster damage evaluation, this study introduces the Visual Prompt Damage Evaluation (ViPDE) framework, a novel contrastive learning-based approach that leverages the embedded knowledge within the Segment Anything Model (SAM) and pairs of remote sensing images to enhance building damage assessment. In this framework, we propose a learnable cascaded Visual Prompt Generator (VPG) that provides semantic visual prompts, guiding SAM to effectively analyze pre- and post-disaster image pairs and construct a nuanced representation of the affected areas at different stages. By keeping the foundation model’s parameters frozen, ViPDE significantly enhances training efficiency compared with traditional full-model fine-tuning methods. This parameter-efficient approach reduces computational costs and accelerates deployment in emergency scenarios. Moreover, our model demonstrates robustness across diverse disaster types and geographic locations. Beyond mere binary assessments, our model distinguishes damage levels with a finer granularity, categorizing them on a scale from 1 (no damage) to 4 (destroyed). Extensive experiments validate the effectiveness of ViPDE, showcasing its superior performance over existing methods. Comparative evaluations demonstrate that ViPDE achieves an F1 score of 0.7014. This foundation model-based approach sets a new benchmark in disaster management. It also pioneers a new practical architectural paradigm for foundation model-based contrastive learning focused on specific objects of interest. Full article

Deep-learning-based methods are crucial for building extraction from high-resolution remote sensing images, playing a key role in applications like natural disaster response, land resource management, and smart city development. However, extracting precise building from complex urban and rural environments remains challenging due to spectral variability and intricate background interference, particularly in densely packed and small buildings. To address these issues, we propose an enhanced U²-Net architecture, MFA-Net, which incorporates two key innovations: a Multidimensional Feature Adjustment (MFA) module that refines feature representations through Cascaded Channel, Spatial, and Multiscale Weighting Mechanisms and a Dynamic Fusion Loss function that enhances edge geometric fidelity. Evaluation on three datasets (Urban, Rural, and WHU) reveals that MFA-Net outperforms existing methods, with average improvements of 6% in F1-score and 7.3% in IoU and an average increase of 9.9% in training time. These advancements significantly improve edge delineation and the segmentation of dense building clusters, making MFA-Net especially beneficial for urban planning and land resource management. Full article

Recent years have been exceptionally turbulent due to various crises such as COVID-19, wars, and natural disasters. We conduct a systematic literature review to address the current state of the regional economic resilience literature, a topic regaining significance amid recent global crises. Considering the findings, we not only conduct the most up-to-date analysis of resilience types but also innovate previous research by collecting and processing data on the spatial and income features of regions, providing statistics about shock coverage, and sharing insights into region types. Additionally, we supplement the systematic literature analysis methodology by experimenting with large language models and defining new search strategies. The results show that most of the literature covers European countries, while that covering all other countries is far behind. Empirical coverage comes from high- and upper-middle-income countries (~97% of research), highlighting the lack of analysis on lower-middle- and low-income countries. This brings into question the applicability of regional resilience policies worldwide. The latest papers still mainly analyze the Great Recession, the most covered shock in the regional economic resilience literature. Not all authors have turned their attention to more recent crises. Finally, we believe future research should focus more on compound resilience—how regional economies cope with cascading or simultaneous shocks. Full article

Urban infrastructure, the lifeline of modern society, consists of inherently multidimensional and interdependent systems that extend beyond various engineered facilities, utilities, and networks. The increasing frequency of extreme events, like floods, typhoons, power outages, and technical failures, has heightened the vulnerability of these infrastructures to cascading disasters. Over the past decade, significant attention has been devoted to understanding urban infrastructure cascading disasters. However, most of them have been limited by one-sided and one-dimensional analyses. A more systematic and scientific methodology is needed to comprehensively profile existing research on urban infrastructure cascading disasters to address this gap. This paper uses scientometric methods to investigate the state-of-the-art research in this area over the past decade. A total of 165 publications from 2014 to 2023 were retrieved from the Web of Science database for in-depth analysis. It has revealed a shift in research focus from single infrastructures to complex, interconnected systems with multidimensional dependencies. In addition, the study of disaster-causing factors has evolved from internal infrastructure failures to a focus on cascading disasters caused by extreme events, highlighting a trend of multi-factor coupling. Furthermore, predicting and modeling cascading disasters, improving infrastructure resilience, and information sharing for collaborative emergency responses have emerged as key strategies in responding to disasters. Overall, the insights gained from this study enhance our understanding of the evolution and current challenges in urban infrastructure cascading disasters. Additionally, this study offers valuable perspectives and directions for policymakers addressing extreme events in this critical area. Full article

This study presents a comprehensive framework for evaluating community-level flood resilience by integrating the fragility of individual buildings, the functionality of critical infrastructure sectors, and their interdependencies. Using performance-based engineering principles, the framework quantifies resilience through isolated building fragility curves, sector-specific functionality fragility curves, and a synthesized community-level functionality model. Applied to a virtual community of 1000 archetypal buildings, the analysis reveals that community functionality decreases with increasing flood depth, reaching a critical threshold of 0.87 at 1.57 m. The sensitivity analysis underscores the importance of accounting for intersectoral dependencies, as they significantly influence community-wide functionality. The results highlight the residential sector’s dominant role in shaping resilience and its cascading effects on other sectors. This framework provides actionable insights for planners and stakeholders, emphasizing the need to prioritize interventions in sectors with the highest vulnerability and dependency to enhance disaster preparedness and response strategies. This framework, novel in its integration of building-level fragility curves with community-wide intersectoral dependencies, provides actionable insights for planners and stakeholders, emphasizing targeted interventions in vulnerable sectors to enhance flood resilience. Full article

Floods, increasingly exacerbated by climate change, are among the most destructive natural disasters globally, necessitating advancements in long-term forecasting to improve risk management. Traditional models struggle with the complex dependencies of hydroclimatic variables and environmental conditions, thus limiting their reliability. This study introduces a novel framework for enhancing flood forecasting accuracy by integrating geo-spatiotemporal analyses, cascading dimensionality reduction, and SageFormer-based multi-step-ahead predictions. The framework efficiently processes satellite-derived data, addressing the curse of dimensionality and focusing on critical long-range spatiotemporal dependencies. SageFormer captures inter- and intra-dependencies within a compressed feature space, making it particularly effective for long-term forecasting. Performance evaluations against LSTM, Transformer, and Informer across three data fusion scenarios reveal substantial improvements in forecasting accuracy, especially in data-scarce basins. The integration of hydroclimate data with attention-based networks and dimensionality reduction demonstrates significant advancements over traditional approaches. The proposed framework combines cascading dimensionality reduction with advanced deep learning, enhancing both interpretability and precision in capturing complex dependencies. By offering a straightforward and reliable approach, this study advances remote sensing applications in hydrological modeling, providing a robust tool for mitigating the impacts of hydroclimatic extremes. Full article