Search Results (102)

In post-disaster relief operations, emergency fleets typically consist of vehicles with varying load capacities, travel speeds, and operating costs. These heterogeneous vehicles are prone to unexpected breakdowns during delivery, which can severely disrupt supply chains and delay urgent aid. Existing scheduling approaches, however, rarely account for fleet heterogeneity, real-time breakdowns, and the trade-off between delivery speed and cost within a unified framework. This paper addresses this gap by formulating the dynamic scheduling of heterogeneous emergency fleets as a two-stage mixed-integer programming model, where total transportation time and cost are simultaneously minimized. The key algorithmic contribution is a fuzzy robust adaptive multi-objective hybrid algorithm (FR-AMOHA) with three interconnected design components. First, a fuzzy evaluation-based pre-matching strategy uses entropy-weighted multi-criteria assessment to generate high-quality initial solutions. Second, a failure-resilient rescheduling module freezes system state upon breakdown detection and selects recovery plans via multi-dimensional resilience scoring to prevent cascading failures. Third, a Pareto-guided adaptive neighborhood search dynamically adjusts operator selection to balance time and cost optimization. Tests on 40 real-world instances with 50 to 1000 demand nodes show that FR-AMOHA achieves optimal inverted generational distance values on 17 out of 40 instances, improves hypervolume by 15% to 35% on average compared with other metaheuristics, and keeps computation times between 40 and 250 s, which is within acceptable limits for emergency decision-making. FR-AMOHA outperforms Gurobi and six leading metaheuristics in solution quality with comparable computational cost. Full article

Against the backdrop of recurrent flood hazards, flood-risk cognition has become an important factor influencing disaster prevention decisions. The flood-risk cognition level of peasant households is related to rural sustainable development and land use patterns in the corresponding region. In this study, a structural equation model (SEM)-based empirical analysis of the flood-risk cognition features and the influencing factors was carried out based on survey data obtained from 685 peasant households with flood threats in southwestern China. The results showed the following: (1) The overall flood-risk cognition of these households was moderate to high. Although 70.22% of the respondents showed a positive attitude toward flood prevention, their pre-disaster preparedness awareness remained insufficient. (2) Pre-disaster preparedness awareness, in-disaster response awareness, and post-disaster restoration awareness had significant positive effects on flood-risk cognition. Specifically, post-disaster restoration awareness mediated the effect of disaster response awareness on flood-risk cognition, disaster response awareness mediated the effect of pre-disaster preparedness awareness on flood-risk cognition, and pre-disaster preparedness awareness moderated the relationship between post-disaster restoration awareness and flood-risk cognition. (3) Clarity of defence knowledge, familiarity with secondary disasters of floods, willingness to evacuate under flood threats, self-ability to solve life difficulties, and mitigation attitudes were important factors influencing flood-risk cognition. (4) Female households outperformed males in flood-risk cognition, and households in Jingyang District showed optimal flood-risk cognition. These findings are significant for the promotion of education regarding disaster prevention and for optimising risk assurance policies in rural areas in southwestern China. Full article

This study explored the preliminary educational effects of a tsunami evacuation program using a streetscape reconstructed from a real district with a 3D laser scanner. The study area was Ono-machi, Kanazawa City, Japan, where 652 scan positions were captured using a Leica BLK360; the resulting point clouds were registered, cleaned, converted into a mesh model, and imported into Unity to build a desktop-based 3D evacuation experience. Twenty-five residents participated, operating the system individually or in small groups, discussing evacuation decisions, and completing pre- and post-experience questionnaires. Exploratory pre–post comparisons using the Wilcoxon signed-rank test were conducted for the 22 complete paired responses. Because five corresponding pairs were tested, the possibility of Type I error inflation due to multiple comparisons was considered. The results were interpreted using both uncorrected p-values and a Bonferroni-adjusted significance threshold of 0.01. The largest improvement was observed in the understanding of hazardous locations, with a mean increase of 1.59 points and a large effect size. The improvement in consideration of detours and alternative routes also remained below the adjusted threshold. Other corresponding item pairs showed positive descriptive changes and uncorrected p-values below 0.05, but they did not meet the Bonferroni-adjusted threshold. Therefore, these findings should be interpreted as preliminary evidence that a locally grounded, point-cloud-derived 3D urban model may support residents’ place-based understanding of local hazards and evacuation-related reflection. By supporting local risk communication, preparedness, and evacuation-related reflection, this approach may contribute to sustainable community-based disaster-prevention education and the development of more resilient coastal communities. Full article

Coal has abundant bedding and joint structures, and most of it exhibits obvious brittle characteristics, which leads to its easy cracking and failure under mining stress. This easily leads to slab cracking and roof collapse in coal mining faces, as well as large deformations in roadways. On-site grouting of fractured coal bodies can effectively prevent these disasters. To reveal this mechanism, this study has first developed a modified ultra-fine cement grouting material and high-pressure continuous grouting system, and then conducted grouting and uniaxial compression tests on post-peak coal samples. Test results indicate that the post-peak residual bearing capacity of grouted coal specimens can recover to 65~85% of the peak strength of intact raw coal. The pre-peak plastic deformation becomes significant, and the post-peak stage exhibits stable strain softening. Grouting is considered to serve to improve the internal stress state of coal samples, act as a ductile grid skeleton, coordinate their internal deformation, and enhance their post-peak bearing capacity. Full article

Extreme weather events exacerbated by global warming pose severe threats to urban safety, underscoring the urgent need for highly accurate precipitation nowcasting. Short-term local heavy precipitation remains a particular challenge for traditional forecasting due to its suddenness and high disaster potential. To address this, we propose a multi-modal fusion framework that integrates ground-based GNSS-derived Precipitable Water Vapor (GNSS-PWV) and ground-based Radar Composite Reflectivity (CR). While GNSS-PWV keenly captures pre-convective atmospheric water vapor accumulation, radar CR details the morphological distribution of hydrometeors. Specifically, we developed the Spatio-Temporal Enhanced Attention Swin U-Net (STEA-Swin) model to synergize these heterogeneous datasets over the Beijing–Tianjin–Hebei region. High-precision PWV was retrieved from 250 Continuously Operating Reference Stations (CORS) using the dual-frequency ionosphere-free Precise Point Positioning (PPP) method, achieving a strong correlation (>0.97) with ERA5 reanalysis data. Validated against measured data from the 2025 flood season, the STEA-Swin model achieved a Probability of Detection (POD) of 0.68 for torrential rain events at a +1 h forecast lead time. Notably, compared to single-source models, the Critical Success Index (CSI) and POD for torrential rain improved by 18.5% and 21.5%, respectively. These findings demonstrate that coupling deep learning with ground-based GNSS-derived atmospheric thermodynamic information can significantly enhance early warning capabilities, providing a promising technical approach for regional disaster prevention and climate resilience. Full article

Underground mine emergencies compromise fixed communication infrastructure exactly when situational awareness is most critical for effective rescue operations. Existing LoRa mesh protocols fail in underground mines because they ignore the structured topology of tunnel networks, specifically the waveguide effect along straight galleries, severe signal discontinuity at junctions, and the dead-end geometry of working faces. This paper presents the Topology-Aware Concurrent LoRa (TACL) mesh protocol, in which each node autonomously infers its structural role from local RF observations and packet header information, without GPS, pre-loaded mine maps, or central coordination. Role classification resolves the contender estimation problem $\left(N_h\right)$ left open in the prior concurrent transmission literature, enabling provably bounded timing offsets before transmission. TACL assigns a spreading factor (SF)12 to dead-end source nodes for maximum link robustness and SF7–SF10 to relay nodes to create the inter-SF orthogonality margin required for concurrent decoding at junction nodes. Monte Carlo simulation of over 2000 trials yields TACL a PDR of 80.5% versus near-zero for all three baselines, confirming that topology-aware SF diversity is the necessary and sufficient mechanism to prevent junction collision collapse. Hardware deployment at the Missouri S&T Experimental Mine yields a 4.0× PDR improvement over the topology-agnostic concurrent transmission (CT)-fixed baseline, a median end-to-end latency of 1815 ms with 84× tighter latency spread than ALOHA-based protocols and 2.5× lower energy per delivered packet. These results establish that explicit exploitation of underground mine topology is essential for reliable, predictable, and energy-efficient emergency mesh communications in post-disaster underground mine scenarios. Full article

The continuous downward mining of close-distance coal seam groups faces severe challenges, yet existing research rarely addresses the structural failure mechanisms in groups with three or more layers. To address this, a two-dimensional physical similarity simulation combined with non-contact digital image correlation (DIC) technology and fractal geometry theory was conducted based on the geological conditions of Donghuantuo Coal Mine. This multi-method approach ensured the high-precision capture and validity of the spatiotemporal deformation data. The evolution of overlying strata and fracture networks during the extraction of four close-distance coal seams was quantified. The results indicate that underlying seam mining triggers severe secondary activation of upper goafs, which transforms the classic vertical three-zone structure into a composite trapezoidal failure zone. Driven by structural instability, the maximum subsidence of the overlying strata exhibits a step-like nonlinear growth, increasing dramatically from an initial 0.44 m to 8.70 m. Simultaneously, the topological evolution of the fracture network exhibits an overall nonlinear increase. Specifically, the fractal dimension rose from an initial value of 1.234 to a more stable value of 1.437, featuring two significant surges with growth rates of 8.34% and 3.79% that directly corresponded to spatial goaf connectivity. The mutual verification between the macroscopic displacement jumps and the fracture network evolution confirms the reliability of the obtained results. Ultimately, the mechanical model of the interlayer rock transitions from a rigid load-bearing beam to a loose buffer layer. Based on these mechanisms, a differentiated interlayer support strategy is proposed. High pre-tension and impact-resistant supports must be applied to the upper seams, whereas pressure-relief and flexible yielding supports are required for the lower seams. This study provides theoretical guidance for disaster prevention in close-distance coal seam groups mining. Full article

Sea surface cooling (SSC) induced by typhoons has a significant impact on typhoon intensity and regional air–sea interaction. This study develops a machine learning model based on a multilayer perceptron (MLP) to predict SSC during typhoon passage over the western North Pacific. The model uses pre-typhoon ocean background conditions and ocean states at the typhoon peak moment as inputs, including wind field, sea level anomaly (SLA), mixed layer depth (MLD), and 100 m water temperature. Trained on historical typhoon data and multi-source ocean observations from 2002 to 2018, the model directly predicts SSC during typhoon events from 2019 to 2020. Results show that the model achieves a mean absolute error (MAE) of 0.379 °C, a root mean square error (RMSE) of 0.488 °C, and a bias of 0.087 °C. The model reproduces the typical rightward bias in SSC spatial distribution. Under normal ocean conditions, such as open deep-water areas with moderate stratification and no strong eddy interference, the model performs well, with errors below 0.1 °C at some points. Although some biases exist under complex ocean environments and abrupt changes in typhoon dynamics, the model still captures the overall cooling trend. This study demonstrates the feasibility of machine learning for typhoon–ocean interaction forecasting. The proposed framework can provide technical support for typhoon intensity forecasting, marine disaster warning, and aquaculture risk prevention. Full article

Bed separation is a common geological phenomenon in the overburden strata during coal mining, which easily induces water inrush hazards, surface subsidence hazards, and other engineering disasters, thus seriously threatening the safety and efficiency of coal mining operations. This paper presents the spatiotemporal evolution characteristics and dynamic prediction of bed separation. The different boundary conditions before and after coal mining disturbance are considered to calculate and predict the location, spatial dimension and spatiotemporal evolution process of bed separation development. Theoretical analysis and scale model tests are used to study the distribution and process of bed separation development with comparisons made between the pre- and post-mining conditions. Formulas for the dynamic prediction of bed separation and a criterion for identifying bed separation development locations are proposed. The vertical propagation coefficient (K_s) and the horizontal development coefficient (K_l) of bed separation are proposed to quantitatively predict the vertical propagation extent and horizontal expansion scale of bed separation space with the advancement of the panel, providing key indicators for the dynamic prediction of bed separation evolution. The results show that the size and duration of bed separation space increase abnormally in the presence of thick and hard strata. This study provides a theoretical basis and practical guidance for the design and optimization of bed separation water hazard prevention and overburden grouting for subsidence control. Full article

Urban transportation systems are facing increasingly severe threats from extreme weather events such as rainstorms, which can trigger cascading failures and lead to regional traffic paralysis. The strategic location of emergency facilities to enhance system resilience has emerged as a critical proactive prevention strategy. This study proposes a multi-objective hierarchical coverage location model that integrates point and flow demands to improve the resilience of urban road traffic systems under rainstorm conditions. First, the resilience risk levels of road nodes were quantified using an entropy-weighted TOPSIS method that combines topological attributes, traffic flow performance, and indirect propagation intensity. Second, a flow-capturing mechanism was introduced to address the dynamic rescue demands of stranded vehicles in motion, enabling the pre-positioning of “safe havens” along critical travel routes. The model balances two objectives: maximizing the resilience risk value of the covered demands and minimizing facility construction costs. A case study was conducted in Jianghan District, Wuhan, a flood-prone area, and the NSGA-II algorithm was employed to solve the multi-objective optimization problem. The results demonstrate that the proposed model significantly outperforms traditional single-demand location models in terms of coverage effectiveness and cost efficiency, achieving improvements in resilience risk coverage of up to 311.6% and cost reductions of up to 63.6%. This study provides a systems science perspective for pre-disaster emergency resource allocation, shifting the paradigm from infrastructure-centric protection to human-centered rescue. Full article

To enhance the power supply resilience of distribution networks with high-penetration distributed photovoltaic (PV) integration during extreme disasters, deploying Mobile Energy Storage Systems (MESSs) proves to be an effective countermeasure. This paper proposes an optimized operational strategy for distribution networks, integrating coordinated clustering of distributed PV systems and MESS operation to ensure power supply during both pre-disaster prevention and post-disaster restoration phases. In the pre-disaster prevention phase, an improved Louvain algorithm is first applied for PV clustering to improve source-load matching efficiency within each cluster, thereby enhancing intra-cluster power supply security. Subsequently, under the worst-case scenarios of PV output fluctuations, a robust optimization algorithm is utilized to optimize the pre-deployment scheme of MESS. In the post-disaster restoration phase, cluster re-partitioning is performed with the goal of minimizing load shedding to ensure power supply, followed by reoptimizing the scheduling of MESS deployment and its charging/discharging power to maximize the improvement of load power supply security. Simulations on a modified IEEE 123-bus distribution network, which includes two MESS units and twenty-four PV systems, demonstrate that the proposed strategy improved the overall restoration rate from 68.98% to 86.89% and increased the PV utilization rate from 47.05% to 86.25% over the baseline case, confirming its significant effectiveness. Full article

In response to the issues of low synergy efficiency between gas extraction and water injection, unclear procedural connections, and high costs in coal mine gas disaster prevention, this paper proposes a collaborative prevention technology for coal mine gas disasters termed “pump–injection–lock.” First, based on the kinetics of gas desorption in gas-bearing coal under different water-bearing conditions, an optimization model for the sequence of gas extraction and high-pressure water injection was developed. This model reduced the gas desorption rate in the experimental area by 32.5% and increased the effective extraction radius of boreholes by 18.7%. Second, based on the coupling relationship between water lock formation pressure, interfacial tension, and pore structure, a criterion model for process transition was constructed, enabling quantifiable identification of the transition node between “pump–injection.” The water lock’s inhibition of gas release duration was improved by over 25% compared to conventional water injection. Finally, by integrating the multiple effects of high-pressure water injection—enhancing permeability, softening, displacement, and flow limitation—a “multi-purpose” synergistic pathway was established. This increased the pre-drainage gas concentration in the test working face by 40%, the pure gas extraction volume by 28%, and reduced gas over-limit incidents by over 50%. Experiments and industrial trials demonstrated that the application of this technology in the 15^# coal seam of Yixin Coal Mine shortened gas extraction by 36%, reduced borehole engineering by 72.8%, eliminated gas over-limit incidents during mining, and cumulatively generated economic benefits exceeding 425 million yuan in the same year, significantly improving the efficiency and cost-effectiveness of gas disaster prevention. Full article

Rock burst is one of the most typical dynamic disasters in the process of coal mining. Energy-absorbing components are key to anti-impact equipment. Exploring the mutual feedback relationship between energy-absorbing components and columns for the prevention and control of roadway rock burst is of great significance. In this study, an arc-shaped energy-absorbing component was designed, and its energy-absorbing characteristics were analyzed. The finite element analysis results of the arc-shaped energy-absorbing component were verified via the crushing test machine. The energy-absorption effect of pre-folded, diameter-expanded, eversion, and arc energy-absorbing columns under impact is compared horizontally. The results show that the supporting force is stable during the crushing deformation of the arc-shaped energy-absorbing component, and the average supporting force measured in the test is 1145.35 kN. Compared with the other three energy-absorbing columns, the arc-shaped energy-absorbing column has a lower emulsion pressure peak and the maximum pressure fluctuation amplitude during the impact process; it also has a better deceleration effect on the quality mass. During the impact process, the influence of the arc-shaped energy-absorbing component on the liquid impact in the column is summarized into three stages, namely approximate elasticity, flexible yield energy-absorbing, and approximate rigidity, which can achieve the peak clipping effect on the liquid impact and improve the impact resistance of the column. Full article

Urban areas face increasing flood risks due to extreme precipitation and anthropogenic activities, which threaten residents’ livelihoods. However, conventional research often lacks a forward-looking perspective, failing to integrate future flood vulnerability assessments with pre-disaster resource allocation. To address this gap, the combination of spatiotemporal flood vulnerability distributions and a pre-disaster funding allocation model serves to enhance urban flood resilience and recovery capabilities. Using Wenzhou City as a case study, a Hydrodynamic Flood Vulnerability Framework (VHCF) was applied to assess current and future vulnerabilities based on hydrodynamic modeling, which revealed distinct spatial patterns in vulnerability. Specifically, a coupled hydrological–hydrodynamic model and the Patch-generating Land Use Simulation (PLUS) model were integrated to simulate flood dynamics under future land-use scenarios for the years 2020 and 2030. A subsequent funding optimization model, based on the VHCF, was developed to prioritize disaster prevention resources for both current and projected high-risk areas. This approach achieves efficient resource allocation by balancing multidimensional flood vulnerability dynamics. The results indicate that extremely high-risk and high-risk zones are predominantly distributed along river corridors and urban centers. From 2020 to 2030, the areal proportion across all vulnerability levels exhibited an increasing trend. Following funding optimization, the coverage rates for low-risk and extremely low-risk zones reached 88.29% and 87.93% in 2020 and 2030, respectively. This methodology provides a scientific basis for decision-makers to enhance urban flood resilience, facilitate post-disaster recovery, and advance sustainable disaster prevention and mitigation strategies. Full article

Accurately estimating urban floodwater depth is a critical step in enhancing urban resilience and strengthening disaster prevention and mitigation capabilities. Traditional methods relying on hydrological monitoring stations and numerical simulations suffer from limitations such as sparse spatial coverage, insufficient validation data, limited accuracy, and delayed fast performance. In contrast, social media data—characterized by its vast volume and fast availability, can effectively compensate for these shortcomings. When processed using artificial intelligence (AI) algorithms, such data can significantly improve credibility, disaster perception speed, and water depth estimation accuracy. To address these challenges, this paper proposes a robust and widely applicable method for rapid urban flood depth perception. The approach integrates AI technology and social media data to construct an AI framework capable of perceiving urban physical parameters through multimodal big data fusion without costly model training. By leveraging the near real-time and widespread nature of social media, an automated web crawler collects flood images and their textual descriptions (including reference objects), eliminating the need for additional hardware investments. The framework uses predefined prompts and pre-trained models to automatically perform relevance verification, duplicate filtering, object detection, and feature extraction, requiring no manual data annotation or model training. With only a minimal amount of water depth annotated data and compressed cross-modal feature vectors as training input, a lightweight Multilayer Perceptron (MLP) achieves high-precision depth estimation based on reference objects. This method avoids the need for large-scale model fine-tuning, allowing rapid training even on devices without GPUs. Experiments demonstrate that the proposed method reduces the Mean Square Error (MSE) by over 80%, processes each image in less than 0.5 s (more than 20 times faster than existing large-model approaches), and exhibits strong robustness to changes in perspective and image quality. The solution is fully compatible with existing infrastructure such as surveillance cameras, offering an efficient and reliable approach for fast flood monitoring in urban hydrology and water engineering applications. Full article