Search Results (758)

Modern smart cities increasingly depend on resilient and intelligent energy infrastructures to maintain critical urban services during large-scale disturbances and multi-fault conditions. Conventional restoration approaches are often limited by centralized operation, delayed response, and inadequate coordination of distributed energy resources (DERs) under emergency conditions. To address these challenges, this paper proposes a Federated AI-Driven Urban Energy Resilience Framework for Smart City Critical Infrastructure Restoration using Virtual Power Plant (VPP) coordination, blockchain-enabled peer-to-peer (P2P) energy trading, and intelligent distributed energy management. The proposed framework is validated on the IEEE 118-bus radial distribution system under severe dual-fault outage conditions, representing urban disaster-induced infrastructure interruptions. Critical urban service zones, including healthcare support systems, emergency loads, smart residential sectors, and EV charging corridors, are considered during the restoration process. The Seagull Optimization Algorithm (SOA) is employed to optimize DER dispatch and improve restoration performance under operational constraints. A progressive restoration strategy comprising conventional outage conditions, VPP-assisted restoration, blockchain-enabled decentralized energy trading, and AI-driven coordinated restoration is analyzed. Simulation results demonstrate that the proposed framework significantly enhances urban energy resilience by increasing load restoration from 55.05% to 94.20%, reducing Energy Not Supplied (ENS), improving voltage stability, and lowering interruption-related economic losses. The minimum bus voltage improves to 0.965 p.u. under the proposed coordinated restoration strategy. The results show that coordinated VPP operation and blockchain-based energy sharing can support reliable restoration of critical urban infrastructure during major outage conditions. The results indicate that integrating AI-assisted VPP coordination with secure decentralized energy trading can effectively support smart city critical infrastructure continuity during extreme outage conditions. The proposed framework provides a scalable and resilient solution for future intelligent urban energy systems and disaster-resilient smart city applications. Full article

In recent years, China’s urban underground rail transit has developed rapidly, and the development of underground space has become increasingly complex, exposing the system to multiple operational risks such as structural instability, excessive deformation, equipment failures and emergencies. Existing studies often evaluate individual hazards or isolated stakeholder risks, while insufficient attention has been paid to how sudden events interact and propagate as disaster chains. To address this gap, this study develops a disaster-chain network framework for operational risk management in underground rail transit. Twenty sudden disaster risk events are first identified through literature review, expert consultation, system investigation, and HAZOP (Hazard and Operability) analysis. A database of 595 historical events is then used to construct co-occurrence and adjacency matrices. And the Jaccard index is used only to quantify association strength, while temporal order, HAZOP-based causal screening, and expert verification are introduced to distinguish plausible triggering relationships from simple correlations. Network indicators, including degree, betweenness, modified clustering coefficient, path length, connectivity, and edge vulnerability, are applied to identify critical nodes and propagation paths. The results indicate that functional failure of civil structures, fire, and crowd stampede are the dominant risk nodes. The proposed framework provides a transparent and replicable basis for prioritizing monitoring, emergency response, and link-cutting mitigation measures. The findings are intended as system-specific decision support rather than universal risk rankings and should be updated when new local operational data become available. Full article

Frequent heavy rainstorm disasters have led to the need for temporary storage of large quantities of heterogeneous disaster-related solid waste within a short period, making temporary storage an important issue in the construction and optimization of the urban comprehensive urban emergency management systems. This study takes the “23·7” catastrophic rainstorm event in Mentougou District, an area prone to rainstorm disasters in Beijing, as a case study and develops an auxiliary decision-making model for site selection that integrates estimates of construction waste and household goods waste, an “initial selection—screening—optimization” suitability evaluation, and the optimization of spatial layout optimization. By combining the spatial analysis method of the Geographic Information System (GIS), an evaluation index system covering natural geography, ecological environment, and socio-economic factors was constructed. An integrated AHP–EWM model was constructed, merging the expert-driven, subjective weighting of the Analytic Hierarchy Process with the objective, data-derived weighting of the Entropy Weight Method to determine indicator weights. The suitability distribution for site selection was studied by combining the multi-factor weighted overlay model, and the area most suitable for construction of Temporary Disaster Waste Storage Sites (TDWSSs), accounting for 4.51% of the total area, was identified. Subsequently, multiple constraints—including ecological protection redlines and minimum area requirements—were superimposed to exclude non-compliant areas. Ultimately, a combined optimization model integrating the minimum facility location model, maximum coverage model, and minimum impedance model was constructed, and the optimal site selection scheme was determined via ArcGIS. The results show that, when seven TDWSSs are considered, the coverage rate of administrative villages within the 20 km transportation service range reaches 97.38%. The results also indicate that, when the number of TDWSSs exceeds eight, the increase in the coverage rate tends to be moderate and the optimization space is limited, indicating that the layout scheme with seven TDWSSs is close to the regional optimal solution. This framework provides crucial guidance for post-rainstorm TDWSS planning and layout optimization. Full article

Climate change and rapid urbanization increasingly threaten water security in large river basins, yet existing assessments often fail to capture the multi-scale interactions between hydroclimatic extremes and human activities. To address this gap, we developed an integrated framework combining risk assessment, multi-method driver diagnosis (Geodetector, Multi-Scale Geographically Weighted Regression (MGWR), and Structural Equation Modeling (SEM)), and Zoned Management. Using a landscape-derived Ecological Risk Index (ERI) as a proxy indicator of runoff and non-point source potential, based on established empirical linkages between landscape metrics and hydrological processes, we applied the framework to three major urban agglomerations in the Yangtze River Basin from 2000 to 2020. Our results reveal three distinct risk mechanisms: in the Chengdu–Chongqing area (CYUA), a 165.8% increase in impervious surfaces drives altered runoff; in the Middle Reaches (MRC), the q-value of the Standardized Precipitation Index (SPI) rose from 0.017 in 2000 to 0.146 in 2020, corresponding to a 759% relative increase. Although the absolute q-value of SPI remains moderate at around 0.15, its rapid rise suggests increasing hydrological sensitivity of the MRC’s river–lake system to precipitation extremes; in the Yangtze River Delta (YRD), socioeconomic activities exert overriding pressure. Based on these diagnostics, we propose tailored strategies for water environment management, adaptive planning, and disaster mitigation. This framework offers a scientific basis for differentiated water governance in large river basins facing coupled anthropogenic and hydroclimatic pressures. Full article

Building instance segmentation in remote sensing imagery supports applications such as urban management, disaster assessment, 3D urban modeling, and land-cover monitoring. However, variations in building scale, dense spatial distribution, complex background textures, shadows, and occlusions make it difficult to balance segmentation accuracy, boundary recovery, inference efficiency, and deployment cost. This study establishes a unified multidimensional benchmark for remote sensing building instance segmentation. The primary benchmark evaluates mask-predicting instance segmentation models, including YOLOv8-seg, YOLOv11-seg, YOLO26-seg, and Mask R-CNN, under consistent training and evaluation settings. RT-DETR-l and RT-DETR-x are retained only as auxiliary detection-only Transformer baselines because they do not output instance masks in the implemented setting. The benchmark covers bounding-box detection, mask-based segmentation, inference efficiency, model complexity, training behavior, and qualitative visualization. To assess cross-dataset transferability and degradation-specific robustness beyond a single dataset, we further conduct zero-shot WHU-to-Inria testing, independent Inria training/testing with different initialization strategies, and controlled degradation tests involving shadow/occlusion and Gaussian blur. Results on WHU and Inria show that high-capacity YOLO-seg models are competitive among the evaluated mask-predicting models. Under the current experimental settings, YOLOv11x-seg achieves the highest or near-highest mask-based accuracy, whereas YOLOv11m-seg provides a favorable balance between accuracy, speed, and complexity. The zero-shot WHU-to-Inria test reveals a clear domain shift, while the Inria in-domain experiments indicate that high-capacity YOLO-seg models recover competitive performance after target-domain training. The controlled degradation tests show a smaller performance drop under shadow/occlusion than under Gaussian blur for YOLOv11x-seg. These findings provide benchmark-specific evidence for selecting remote sensing building instance segmentation models under accuracy-oriented and efficiency-oriented deployment requirements. Full article

Urban flood susceptibility mapping is essential for disaster risk management in rapidly urbanizing regions. Although high-resolution Earth observation (EO) data provide detailed information for fine-scale flood analysis, existing studies are often limited by inadequate representation of drainage capacity, inappropriate spatial scales, and model uncertainty under sparse flood sample conditions. To address these issues, this study develops a multi-scale urban flood susceptibility mapping framework based on the Maximum Entropy (MaxEnt) model, integrating multi-source high-resolution geospatial data. A three-tier spatial unit system, including catchment, street, and grid scales, was constructed. Two models were developed at each scale using per capita drainage density (PCDD) and pipe density (PipeDen) as drainage capacity indicators. The results reveal significant scale-dependent differences in spatial autocorrelation, model performance, and variable responses. Compared with the PipeDen-based model, the standard deviation of AUC decreased by 37.5% and 25.0% at the catchment and street scales, respectively, and the model produced a more physically consistent relationship between drainage capacity and urban flood susceptibility. Considering the combined results of model performance, spatial autocorrelation, and response-curve analysis, the street scale PCDD-based model achieved the best overall performance among the six multi-scale models. Impervious area ratio, distance to roads, and annual maximum daily precipitation were identified as dominant factors influencing urban flood susceptibility. Based on the optimal street scale PCDD-based model, a 2 m resolution susceptibility map was generated, showing that high-susceptibility areas are mainly concentrated in highly urbanized central districts and along major transportation corridors. This study highlights the importance of spatial scale and drainage capacity representation in high-resolution urban flood susceptibility mapping. Full article

Rapid urbanisation in the Global South has accelerated the expansion of informal settlements. This has increased exposure to water-, fire-, and health-related risks and undermined pathways toward sustainable urban development. Although such risks are often framed as environmental outcomes, growing evidence suggests that they are fundamentally shaped by spatial planning conditions. This study investigates the spatial planning drivers of disaster risks in informal settlements in Mopani District, South Africa. An exploratory mixed-methods design was adopted, combining data from 605 households and 87 key informants. Quantitative data were analysed using exploratory factor analysis and multinomial logistic regression, while qualitative data were analysed thematically. The results show that water-related risks were the most prevalent, affecting 50.7% of households, followed by health risks (26.3%) and fire risks (14.7%). Activity patterns emerged as the strongest and most consistent predictor of disaster risk outcomes. The findings demonstrate that disaster risk is systematically shaped by the spatial organisation of settlements, activity concentration, built-environment conditions, and institutional limitations. These dynamics have direct implications for urban sustainability. The study contributes to the literature by advancing a systems-based spatial planning perspective on disaster risk in informal settlements and by providing empirical evidence from South Africa on the persistent gap between the policy intentions of SPLUMA and its implementation. It further highlights that achieving sustainable and resilient cities requires a shift from reactive disaster management towards proactive, risk-sensitive spatial planning approaches that integrate informal settlements into formal planning systems. Full article

As global climate change intensifies, typhoon disasters pose growing threats to the socio-economic stability of coastal cities. Quantifying urban typhoon resilience and identifying its spatial driving mechanisms are essential for informing targeted disaster risk management and built environment optimization. This study develops an NTL-based framework to quantify urban typhoon resilience across three major typhoon events in Zhuhai from 2017 to 2020, using NTL loss rate and NTL recovery time as the primary resilience indicators and NTL loss as a descriptive measure of absolute disaster impact magnitude. OLS and GWR models are then applied to a 20-factor indicator system to identify the global drivers of resistance and recovery capacity and uncover the spatial heterogeneity of their effects across urbanization gradients, with the aim of providing both a replicable methodological framework and an empirical basis to inform differentiated resilience optimization strategies for coastal cities. The results demonstrate that urban typhoon resilience varies systematically across urbanization gradients in both dimensions. Highly urbanized areas consistently show stronger resistance, with NTL loss rates of 32–46% versus 36–50% in low-urbanized areas, as well as faster recovery, with NTL recovery times of 2.6–3.8 days versus 2.9–5.6 days. Transportation infrastructure emerges as the most consistent global driver. GWR reveals that its effects are most pronounced in less urbanized areas, where the absolute coefficient for transport station density reaches 4.804 (over 4% higher than in other zones). Blue–green infrastructure also plays a significant role, with higher NDVI values being associated with shorter recovery times. These findings provide a replicable NTL-based methodological framework and spatially explicit empirical evidence to support targeted and differentiated resilience optimization in coastal cities. Full article

Global climate change and extreme precipitation events increasingly challenge urban infrastructure resilience, particularly in topographically vulnerable regions like Taiwan. Traditional flood monitoring relies heavily on the manual visual interpretation of extensive surveillance networks, a process that imposes high cognitive loads and risks delayed emergency responses. This study presents a comprehensive Cyber-Physical System (CPS) architecture for an automated Water Image Monitoring Platform. Integrating approximately 10,000 cameras and multi-modal data—including precipitation records and spatial alerts—the platform leverages advanced semantic segmentation (DeepLabV3+ with Xception71) to delineate inundation boundaries. To ensure robustness under adverse conditions such as low illumination, fog, and specular glare, we implemented targeted optimizations, including HSV pre-processing, Deblur GAN architectures, and attention mechanisms. Results demonstrate a significant performance evolution, with the event recall rate rising from 88% in 2022 to 99.7% by 2025. A key driver of this success is the synergy between stationary nodes and vehicle-mounted CCTV units, which provide critical dynamic geographic coverage. Furthermore, the deployment of edge computing reduced warning latency 10 times—from 19.2 to 2 s—while virtual water level gauges maintained a mean error within ±10 cm. Despite these gains, a Human-in-the-Loop (HITL) architecture remains strategically necessary for ethical accountability and error filtering. This CPS provides a foundational model for autonomous, resilient urban disaster management. Full article

Under the combined impacts of climate change and urbanization, flood disasters have exhibited increasing non-stationarity, low-frequency but high-impact characteristics, and enhanced spatial dependence. Traditional indemnity-based flood insurance has certain limitations in claim efficiency and loss assessment. In contrast, index-based flood insurance, characterized by objective triggering mechanisms, rapid claim settlement, and low operational costs, has gradually become an important tool for flood catastrophe risk management. Based on a literature review approach, this study systematically reviews the index system, pricing mechanisms, and basis risk of index-based flood insurance, and provides a comprehensive analysis from the perspectives of index construction, threshold determination, and payout design. The results indicate that index systems have evolved from single hazard indicators to coupled indices integrating hazard characteristics and loss information, and multiple pricing approaches have been developed, including fixed, linear, piecewise payout, and probabilistic payout schemes (payouts determined by loss probabilities rather than fixed thresholds). Among the reviewed approaches, inundation-area-based indices generally show stronger consistency with actual losses at urban scales, whereas precipitation-based indices are more suitable for large-scale regional applications due to their rapid triggering capability. However, basis risk remains a critical issue, mainly arising from index errors, spatial scale mismatches, and inappropriate threshold settings. Therefore, to address the identified limitations of basis risk, threshold uncertainty, and spatial mismatches, future research should focus on multi-dimensional risk indices, dynamic threshold setting, and optimized spatial risk zoning, as well as the integration of remote sensing and machine learning methods to improve the consistency between indices and actual losses. The findings provide practical guidance for insurers in product design, for policymakers in regional flood risk financing, and for disaster managers in improving climate adaptation strategies. Full article

Robust tsunami casualty estimation is vital for Peru’s central coast. While static maps ignore evacuation dynamics, precise agent-based models (ABMs) are often too computationally demanding for rapid screening. To bridge this gap, we propose an efficient geospatial workflow coupling TUNAMI-N2 simulations with shortest-path routing. Evaluating four subduction scenarios across Chorrillos and Villa El Salvador, the model tracks census-block evacuation progress. By intersecting evacuation trajectories with tsunami arrival times, casualties are calculated using empirical depth-dependent fragility functions. Results highlight that delayed reaction times significantly increase mortality. Furthermore, a counterintuitive dynamic emerges in spatially constrained corridors lacking vertical evacuation: higher walking speeds can paradoxically increase fatalities by advancing evacuees into deeper inundation zones before being overtaken. This highlights that behavioral preparedness must be coupled with structural urban interventions. Ultimately, our scalable approach enables DRR (Disaster Risk Reduction) managers to rapidly map mortality hotspots and prioritize critical infrastructure improvements in highly exposed coastal zones. Full article

Ensuring the sustainability of ageing water-storage infrastructure is an increasingly urgent challenge under climate-driven hydrological extremes. In the Republic of Korea, approximately 18,000 small and medium-sized agricultural reservoirs—many several decades old—pose escalating risks to downstream communities and threaten progress toward SDGs 6, 11, and 13. This study presents a 0.5 m airborne LiDAR-based, GPU-accelerated two-dimensional shallow-water simulation of a hypothetical breach of the Geumosan Reservoir, South Korea, using a MUSCL + HLL solver verified against the Ritter (1892) and Stoker (1957) analytical dam-break solutions. Two scenarios are compared: Run A with a uniform Manning coefficient (n = 0.035) and Run B with spatially variable roughness derived from the Korean Ministry of Environment land-cover map (mean n = 0.0711). Mass conservation is preserved to within 0.01% during the closed-domain phase. Spatially variable roughness expands the total inundated area by 8.5% (3.05 → 3.31 km²) while reducing the Extreme-hazard zone, defined by the DEFRA hazard rating HR = h(v + 0.5), by 24% (1.49 → 1.14 km²); arrival times in the downstream urban corridor are delayed by up to 30 min. Uniform Manning assumptions therefore systematically overestimate extreme-hazard extents while underestimating the broader shallow-inundation footprint—biases comparable in magnitude to breach-parameter uncertainty. By delivering reproducible, georeferenced hazard, arrival-time, and damage-class maps for emergency action planning, the proposed framework supports risk-informed and sustainable management of ageing reservoir infrastructure and community-level disaster resilience aligned with the Sendai Framework and SDGs 6, 11, and 13. Full article

Short-term precipitation forecasting is essential for disaster prevention, urban management, and weather-sensitive decision making, yet radar-based nowcasting remains challenging because precipitation systems evolve nonlinearly and high-frequency echo structures are easily over-smoothed by deterministic sequence models. This paper proposes a ViT-modulated diffusion spatiotemporal prediction network (VSTPN) that cascades a spatiotemporal prediction module with a ViT-conditioned diffusion refinement module. The spatiotemporal module models the temporal evolution of radar echoes, whereas the ViT-Diffusion module uses global contextual features as conditional guidance during iterative denoising to refine spatial structures. Experiments on the HKO-7 benchmark show that VSTPN achieves lower MSE and higher SSIM than the tested baselines and improves CSI, HSS, and POD at the evaluated reflectivity thresholds. At the 40 dBZ threshold, the model improves CSI, HSS, and POD, while its FAR is slightly higher than that of ETCJ-PredNet, indicating a recall–false alarm trade-off for intense echoes. Additional post-hoc diagnostic analyses of relative gains, metric consistency, threshold sensitivity, and component effect sizes further support the stability of the reported improvements under the current experimental protocol. The results suggest that coupling spatiotemporal sequence modeling with diffusion-based radar echo refinement is a feasible direction for short-term precipitation forecasting; nevertheless, probabilistic uncertainty evaluation, multi-domain validation, and additional generative-quality metrics remain important directions for future work. Full article

Short-term precipitation forecasting is an important research direction in meteorological studies, holding significant implications for disaster prevention and mitigation, urban flood drainage, and agricultural meteorological management. Existing deep learning models have achieved favourable results in modeling local features, yet they generally suffer from insufficient sensitivity to heavy precipitation areas, limitations in modeling temporal dependencies, and gradient instability issues. To address these limitations, we propose a novel spatiotemporal dual-branch neural network (ST-DualNet) for short-term precipitation forecasting based on radar echo maps. The network comprises a temporal branch (based on an enhanced ST-DConvLSTM) and a spatial branch (based on dilated convolutions and Transformer), respectively capturing the dynamic evolution and spatial structural features of precipitation. The two branches are integrated through the CBAM attention module and 3D convolution layer to achieve cross-branch feature fusion and prediction output. Experimental results demonstrate that ST-DualNet outperforms multiple mainstream models on the KNMI radar precipitation dataset, especially in heavy precipitation forecasting, providing an effective new framework for short-term precipitation forecasting. Full article

Disaster risk reduction and management are essential for sustainable development in territories highly exposed and vulnerable to natural hazards. Recent disasters in the Canary Islands have highlighted the importance of proactive preparedness and systemic approaches to risk management, emphasizing the need to better understand existing barriers to disaster risk reduction (DRR). This study develops an analysis of risk governance within the current planning instruments in the Canary Islands, the island of Tenerife, and the municipality of Candelaria. The research examines the integration of DRR across strategic, territorial, urban, and emergency planning at the regional, insular, and municipal levels. The findings identify key challenges and opportunities for integrating DRR within existing planning frameworks, highlighting both the potential and the limitations of current instruments as cross-cutting tools for building more resilient territories. While Tenerife has a relatively solid administrative and planning structure that could support a more systemic vision of risk, sectoral fragmentation and coordination gaps remain. Overall, the study contributes to the ongoing discussion on advancing risk governance from a systemic perspective at the local level. The challenges identified delineate the boundaries and directions for improvement, offering a valuable contribution to the existing body of knowledge. Full article