Search Results (1,822)

Satellite precipitation products like GPM IMERG are crucial for hydrological modeling and disaster prevention; yet, their reliability in complex mountainous monsoon regions remains challenging. While the latest IMERG V07 introduces key upgrades, including a Climatological Calibration Algorithm (CCA), its cross-scale error propagation mechanisms and performance heterogeneity in complex underlying surfaces are poorly understood. This study evaluates the daily and monthly performance of IMERG V07 and V06 (Early, Late, and Final Runs) from 2014 to 2020 against 91 rain gauges in Guangxi, China—a typical mountainous monsoon region. The evaluation employs multiple statistical metrics and a multi-dimensional stratification approach based on elevation, precipitation intensity, and seasonality to quantify error propagation and climate-topography coupling effects. The results reveal that V07, particularly the Late Run, enhances daily precipitation detection capabilities, it significantly increases the proportion of systematic positive bias from 62.3 to 64.8% (V06) to 67.2–68.9% (V07). Consequently, upon temporal aggregation to the monthly scale, this systematic overestimation is severely amplified, leading to degraded performance, with the Final Run suffering the most substantial accuracy loss. Furthermore, retrieval accuracy is heavily constrained by surface heterogeneity, with systematic overestimation surging in areas where relatively dry (mean annual precipitation < 1300 mm) and complex terrain (elevation 100–500 m) coincide. The introduced CCA effectively improved dry season estimations but failed during wet season by introducing substantial positive biases. Ultimately, while V07 better captures short-term precipitation dynamics, its structural systematic biases compromise long-term cumulative reliability, highlighting the necessity for physics-based bias correction in hydrological applications and dynamic calibration in future algorithm upgrades. Full article

The layout of urban blue, green and grey spaces plays a crucial role in flood risk, but the potential trade-offs and synergistic effects of these spaces on urban flood risk have not been fully studied. This research uses the spatial Durbin model to analyze the direct impact of changes in urban blue, green and grey spaces on risk changes and the spatial spillover effects, revealing how the balance and coordination states and their changes affect urban flood risk. The results show the following: (1) An increase in the balance effect is often accompanied by an increase in risk, but there are differences in the intensity and direction of response at different stages, and the risk changes show significant spatial dependence and spillover effects. (3) The analysis of the spatial Durbin model indicates that the increase in blue and green spaces mainly alleviates risks through local retention and storage. It is notable that in the later stage, the neighborhood spillover effect of green spaces has exceeded its direct local effect, highlighting the collaborative value of green network connectivity at the regional level; while the impact of grey spaces is the most complex—although the construction of sponge cities and urban rainwater management have led to a negative direct local effect (2020–2024), its positive neighborhood spillover effect still persists. Clearly understanding how “balance and coordination” affects risk at different development stages can help urban managers formulate differentiated strategies for the proportion of blue, green and grey spaces based on the development stage of the city, thereby avoiding the use of “one-size-fits-all” planning standards. Full article

Surface fissures induced by shallow coal seam mining create interconnected pathways for ambient air leakage, significantly aggravating coal spontaneous combustion (CSC) risks in goafs. However, the spatiotemporal evolution of these fractures and the quantitative dynamics of air leakage under repeated mining conditions remain poorly understood. This study investigates the evolutionary laws of mining-induced cracks and air leakage behaviors through laboratory physical similarity simulations and field tracer gas testing. The results demonstrate that during repeated extraction, vertical fractures in the goaf boundaries undergo an expansion-to-stabilization process with significantly increased widths, whereas fractures in the central region experience a process from expansion to closure and stabilization. Crucially, the fracturing of the inter-seam key stratum marks a vital milestone where the upper and lower goafs merge into a complex goaf, precipitating a sudden, sharp surge in air leakage volume. Field observations categorize surface cracks into graben type, collapse type, and tensile type. Graben-type and collapse-type cracks act as the principal pathways for surface air infiltration, collectively forming a rectangular distribution network across the goaf. These findings provide a critical theoretical framework and practical guidance for predicting and controlling surface air leakage disasters in close-distance shallow seam mining. Full article

The heterogeneous occurrence of coal-seam thickness represents a common geological characteristic in underground mining. Variations in coal thickness can directly alter the instability-failure behavior of coal–rock systems, thereby triggering various dynamic disasters. Therefore, revealing failure and disaster-inducing mechanisms of coal–rock systems dominated by coal-thickness effects is critical for deep mining engineering design as well as dynamic disaster prevention and control. To this end, uniaxial compression tests combined with acoustic emission (AE) monitoring were performed on coal–rock combinations with different coal thicknesses. The evolution laws of characteristic strengths (uniaxial compressive strength, initiation strength, and damage strength) versus coal thickness were systematically analyzed. Using full-process spatial localization of internal damage derived from absolute AE energy, an instability evolution model for coal–rock combinations was established. Furthermore, intrinsic disaster-inducing mechanisms governing coal–rock system instability under coal-thickness regulation were summarized, with corresponding engineering prevention-control suggestions put forward. The results show that: (1) UCS, initiation strength, and damage strength of specimens exhibit a nonlinear negative correlation with coal thickness. Initiation strength and damage strength account for approximately 50% and 75% of UCS, respectively; (2) Increasing coal thickness weakens the confinement effect of upper- and lower-sandstone, which shifts the dominant failure zone gradually from coal–rock interfaces to coal interiors. Meanwhile, internal energy accumulation-release processes of combinations present staged evolution characteristics; (3) Different coal thicknesses produce distinct disaster-evolution paths for coal–rock systems. Larger coal thickness corresponds to higher risks of high-energy dynamic disasters. Accordingly, a differentiated hierarchical prevention strategy of “thin protection, medium pressure relief, and thick control” was proposed. These findings provide a theoretical basis for mine engineering design and dynamic disaster prevention-control under dominant coal-thickness effects. Full article

In the mining process of shallow-buried and close-distance coal seam groups in western China, the interconnected collapse fractures between the overlying goaf and the surface form large-area composite goafs, which aggravate surface air leakage and elevate oxygen levels within the goaf. This, in turn, leads to hazardous conditions such as CO over-limits and O₂ deficiency at the working face’s return air corner, which seriously threatens the respiratory health of underground operators and the safe production of mines. Taking the 104 working face of a coal mine in Shenfu-Dongsheng Mining Area as the engineering background, this paper comprehensively adopts SF₆ tracer gas test, fuzzy cluster analysis, and CFD numerical simulation methods to systematically study the distribution characteristics of three-dimensional air leakage channels in composite goafs and their influence mechanism on gas migration in goafs, and proposes a dynamic pressure equalization ventilation (PEV) regulation technology system. The research results show that a multi-dimensional three-dimensional air leakage channel of “surface-interlayer-own layer-roadway” exists in the research area, in which the surface fracture air leakage velocity is about 0.068 m/s, and the interlayer and internal goaf air leakage velocity is about 0.384 m/s. The atmospheric pressure difference between the working face and the surface is the main controlling factor inducing the O₂ deficiency disaster of the working face. Every 100 Pa change in atmospheric pressure difference causes an O₂ concentration fluctuation of about 0.30% at the return air corner, and the critical pressure difference for activating PEV is determined to be 300 Pa. Setting the PEV regulation point at the return air outlet of the working face and adopting the combined dynamic regulation system of fans and air windows can realize accurate pressure balance between the working face and the overlying composite goaf. Full article

Remote sensing building change detection is important for monitoring urban expansion, rural settlement dynamics, post-disaster reconstruction, and human-induced land transformation. However, most existing change detection models are optimized under in-domain protocols, while practical deployment often requires direct transfer from one labeled source dataset to unseen target domains without target images, labels, validation data, adaptation, or threshold calibration. This source-only cross-dataset setting is challenging because changes in sensor characteristics, spatial resolution, viewing geometry, scene composition, and background appearance can cause missed detections and pseudo-change false alarms. To address this problem, we propose DLV-CD, a frozen-DINOv3-based framework that trains only task-specific adapters, a multi-level difference fusion decoder, and a hierarchical evidence fusion module for source-only cross-dataset building change detection. Transfer evaluation on four datasets, namely LEVIR-CD, WHU-CD, S2Looking, and DSIFN-CD, shows that DLV-CD achieves the best F1-score compared with seven reproduced baselines, including classic supervised CD models, recent supervised CD models, and the SAM-based foundation-model baseline SAM-CD. Specifically, DLV-CD improves the average F1-score from 32.99% to 65.38%, outperforming the strongest reproduced baseline by 32.39 percentage points. Precision–recall analysis and qualitative comparisons further show that DLV-CD reduces both recall collapse and pseudo-change over-detection. These results demonstrate that frozen visual foundation representations provide a strong basis for target-free cross-dataset building change detection. Full article

Clarifying the dynamic mechanism of rock pillar fracture and its rock burst-inducing mechanism is fundamental for the prevention and control of rock burst disasters in steeply inclined extra-thick coal seam groups. In this study, field monitoring, theoretical analysis, and numerical simulation were combined to investigate the rebound vibration behavior and rock burst-inducing mechanism of fractured rock pillars, and corresponding mitigation measures for rock pillar-induced rock bursts were proposed. The results indicate that instantaneous rock pillar fracture induces reciprocating rebound vibration behavior within the coal seam rock pillar, causing the velocity, displacement, and strain energy density of the rock pillar to remain in a persistent fluctuation state. Meanwhile, periodic mutual conversion between strain energy and kinetic energy occurs throughout the vibration process. During any vibration cycle, the rock pillar cannot recover to its initial equilibrium position, resulting in a sharp increase in the loads acting on the floor side of the B3–6 coal seam and a significant decrease in the loads acting on the roof side of the B1–2 coal seam. Consequently, the B3–6 coal seam remains subjected to transient dynamic loading, whereas the B1–2 coal seam experiences transient unloading after rock pillar fracture. This asymmetric transient loading mechanism is identified as the intrinsic reason for the higher rock burst proneness of the B3–6 coal seam. Based on the dynamic response characteristics of the stope coal rock system, staggered-level mining of the B1–2 and B3–6 coal seams and slotting presplitting in the B3 roadway were proposed as mitigation measures for rock pillar-induced rock bursts. When the stagger distance of the working face increases from 25 m to 100 m, the stress drop of the B3–6 coal seam is 22.9%~28.7%. When the slotting depth of the rock pillar increases from 25 m to 80 m, the stress of the B3–6 coal seam decreases by 9.88%~24.1%. These findings provide theoretical support and engineering guidance for rock burst prevention and control in steeply inclined coal seam. Full article

The Guangdong–Hong Kong–Macao Greater Bay Area has high building density and diverse project types, and building disaster governance features cross-regional, multi-stakeholder and strongly uncertain characteristics. To reveal the multi-agent strategic interactions after embedding AI digital technologies into the governance process, this paper constructs a three-party stochastic evolutionary game model among public regulators, construction firms and AI technology providers, and introduces multiplicative Gaussian white noise with boundary degradation into the replicator dynamics. The study finds that (1) under baseline parameters, the system evolves toward the state “coordinated strong regulation–AI-compliant governance–high-quality supply”; noise below the local mean-square stability threshold does not change the direction of recovery near the equilibrium but enlarges short-term fluctuations of stochastic trajectories; (2) there is synergistic transmission in the initial strategies of the three parties, with firms’ compliance probability linking both the regulatory and technology sides; (3) governance performance benefits, cross-regional coordination gains, firms’ digitalization gains and high-quality technology subsidies each form positive incentives at different links, while firms’ AI retrofit costs directly depress compliance returns; (4) the mechanism shown on the original parameter surface indicates that higher AI retrofit costs first suppress firms’ AI-compliant governance and, via firms’ demand transmission, affect high-quality technology supply, whereas the response of public regulators is relatively weak. The findings provide mechanism-level hypotheses for phased incentives, cross jurisdictional data collaboration, technology quality assurance, and adaptive regulation. Because the parameters are dimensionless and uncalibrated, project or jurisdiction specific policy magnitudes require empirical estimation and validation. Full article

Geospatial data provide an important basis for urban governance, resource management, disaster assessment, and public health analysis by linking spatial locations, attribute information, and dynamic processes. However, complex geospatial analysis still requires substantial expertise in spatial databases, spatial SQL, and GIS computation tools. Although large language model-based Text-to-SQL methods have lowered the barrier to natural language-driven data querying, most existing approaches rely on single-step SQL generation and remain unstable for spatial tasks that involve attribute retrieval, spatial relationship evaluation, geometric operations, and statistical aggregation. To address this limitation, this paper proposes a Code Generation-Driven Query–Computation Separation Framework (CGD-QCSF). The framework is based on the separation of query and computation, and decomposes complex geospatial analysis into a staged execution process. CGD-QCSF coordinates intent understanding, schema pre-filtering, planning, execution state management, SQL generation, and spatiotemporal computation. A structured planner and an execution state manager coordinate task decomposition, capability-aware routing, and evidence-based recovery. A SQL Code Generation Agent (SCGA) handles database access, attribute filtering, and intermediate data extraction, while a Spatiotemporal Computation Agent (STCA) performs out-of-database spatial computation and statistical aggregation in an isolated Python sandbox. We construct a benchmark of 200 tasks, covering easy, medium, and hard spatial tasks. In the main experiment with Qwen3.7-Plus as the foundation model, CGD-QCSF achieves a Strict Structured Accuracy (SSA) of 90.5%. Removing the Planner reduces SSA to 84.5%, while removing the STCA reduces it to 70.5%. The ablation experiments show that removing either the Python sandbox or the Planner Agent degrades performance on complex tasks. These results indicate that CGD-QCSF extends complex geospatial analysis from single-step SQL generation into a multi-staged execution process. By explicitly separating query and computation, the framework reduces interference between spatial computation logic and database schema information, thereby improving the stability and success rate of natural language-driven geospatial analysis. Full article

Following extreme disasters, hybrid wind farms comprising grid-following (GFL) and grid-forming (GFM) turbines can serve as black-start resources for power system restoration. However, wind power uncertainty, wake effects, and collection-grid restoration constraints complicate the startup sequence planning of GFL turbines within hybrid wind farms. To address these challenges, this paper proposes a multi-objective mixed-integer linear programming (MILP) model that jointly considers electrical impedance paths, wind uncertainty, and spatial wake effects. Information Gap Decision Theory (IGDT) is incorporated into active power support constraints to account for wind uncertainty through a robust adjustment of available generation capacity, while the Dijkstra algorithm is employed to convert collection-cable parameters into impedance-based cost factors for identifying minimum-impedance restoration paths and mitigating transient overvoltage risks. In addition, dynamic wake losses under non-uniform turbine layouts are quantified to capture the influence of startup sequences on local flow fields, and the resulting nonlinear terms are reformulated using the big-M linearization technique. Case studies considering different wind directions, time-varying wind speed conditions, and cable-impedance sensitivities demonstrate the effectiveness of the proposed framework. The results show that the proposed strategy provides a favorable balance between impedance-cost minimization, wake-effect mitigation, and robustness enhancement, while maintaining reliable restoration performance under diverse operating conditions. Full article

This study systematically evaluates the performance of GPM_IMERG and GSMaP_Gauge hourly precipitation products in typhoon rainfall over Mainland China. Using hourly gauge observations from the China Meteorological Administration (CMA)’s national basic meteorological stations as reference, 32 landfalling typhoons during 2021–2025 are analyzed. A multi-layered evaluation framework is established based on 50 km annular stratification from 0 to 500 km relative to typhoon centers, multiple statistical metrics, and dual thresholds for light rain and extreme precipitation. Results indicate systematic underestimation of typhoon rainfall by both products, with GSMaP_Gauge exhibiting more severe negative bias that intensifies nonlinearly with increasing rainfall intensity. Spatially, widespread overestimation occurs in North China, while underestimation dominates elsewhere, with large negative biases concentrated in high-observation regions. Monthly variations show predominantly negative deviations across most months, with GSMaP_Gauge demonstrating persistent negative anomalies except for sporadic positive outliers. Regarding precipitation detection capability, both products perform adequately for light rain, but their capability to capture extreme precipitation remains rather limited, as evidenced by sharply declining Critical Success Index (CSI) across all distance ranges and omission of over 60% extreme precipitation events. GPM_IMERG shows only sporadic high CSI values in the inner-core region during June and October. Error distributions exhibit significant spatiotemporal non-stationarity: errors attenuate markedly within 0–100 km of typhoon centers; seasonally, June and September show higher correlation coefficients but larger RMSE, whereas August presents lower correlation yet smaller errors; diurnally, the 0–50 km zone displays a “three-peak–two-valley” pattern with error maxima in the afternoon, early morning, and evening. In conclusion, both products estimate light typhoon precipitation with reasonable accuracy but still have considerable room for improvement in estimating heavy and extreme rainfall. Dynamic error models based on three-dimensional stratification of distance–season–diurnal phase, coupled with bias correction, are imperative before their application to hydrometeorological modeling, disaster investigation, and climate research. Full article

Post-disaster temporary housing (PDTH) plays a central role in bridging emergency response and long-term recovery, yet its implementation is affected by a wide range of institutional, economic, site-related, technical, and environmental risks. Existing studies commonly examine only one or a limited number of these risk categories, making it difficult to compare their relative importance within a common analytical framework. This study addresses this limitation by developing and empirically evaluating an integrated risk framework for PDTH. A systematic literature review based on the PRISMA protocol was conducted to identify relevant risks, followed by a questionnaire survey of construction professionals. Principal component and confirmatory factor analyses were employed to identify the underlying risk dimensions and assess the fit and measurement properties of the resulting structure. Meanwhile, fuzzy synthetic evaluation was utilized to determine the relative importance of these dimensions. Following significance-index screening and cross-loading assessment, 29 risks were retained and grouped into four dimensions: institutional and governance risks; economic and lifecycle risks; site planning and infrastructure risks; and design and environmental performance risks. The normalized coefficients were closely clustered, ranging from 0.247 to 0.255. Institutional and governance risks had the largest numerical coefficient (0.255), followed by economic and lifecycle risks (0.250), site planning and infrastructure risks (0.248), and design and environmental performance risks (0.247). The narrow spread indicates that respondents assigned broadly comparable importance to all four dimensions, rather than identifying a single dominant risk area. The findings, therefore, point to the need for a balanced approach to PDTH risk management across governance, economic, site-related, and design-related concerns. The study contributes an empirically supported classification and prioritization framework that brings previously fragmented risk categories into a single assessment structure. The analysis does not establish causal relationships or dynamic interactions among the identified risks; rather, it provides a basis for their systematic comparison and for more detailed investigation of risk interdependencies in future research. Full article

Lagos, Nigeria (population c.18.3–25 million), occupies a position of compound exposure at the intersection of accelerating climate hazards, recurring displacement and urban fragility, and governance constraints that have amplified rather than mitigated disaster risk. Conventional single-hazard planning paradigms have been demonstrably insufficient to address the structural drivers of this vulnerability, which emerge from the interaction among biophysical exposure, colonial spatial legacies, post-independence governance deficits, and inequities within the global climate finance architecture. This article employs a structured, systematic literature review of 74 peer-reviewed, institutional and policy sources, conducted through a transparent three-stage protocol involving keyword-based Boolean searches of Scopus, Web of Science, Google Scholar, and the African Journals OnLine (AJOL) database, title–abstract screening, full-text eligibility assessment against pre-specified criteria, and thematic synthesis using a combined deductive–inductive coding framework, reported in accordance with the PRISMA 2020 guidelines. Thematic analysis yields four principal findings: Lagos’s flood and displacement cycles are products of structurally produced vulnerability; compound climate–displacement dynamics are systematically underestimated by single-hazard frameworks; nature-based solutions, community-led upgrading, and participatory governance show measurable effectiveness under enabling governance conditions; and transformative resilience approaches are constrained by political economy and land tenure dynamics. An original Integrated Urban Resilience Framework (IURF) incorporating five pillars is proposed and conceptually benchmarked against twelve comparable Global South frameworks. Post-disaster urban futures in rapidly urbanising megacities require technically rigorous, politically grounded, and ecologically integrated planning frameworks. The IURF offers a replicable conceptual scaffold for cities facing compound climate–displacement risk, subject to future empirical testing. Potential applications of the framework for state planning agencies, multilateral and bilateral funders, community organisations, and comparable West African coastal cities are specified, and the limitations of the review are set out in full. Full article

Identifying coal fire risk areas is essential for safe production in coal mines. Land Surface Temperature (LST) retrieval and high-temperature anomaly extraction are core techniques for coal fire risk detection. To address the insufficient evaluation of the accuracy of relevant algorithms for arid open-pit mines, this study takes the Xingsheng Open-Pit Coal Mine in Yiwu County, Xinjiang as the research object. Based on Landsat imagery and UAV thermal infrared data, we systematically compared five mainstream LST retrieval algorithms and six high-temperature anomaly extraction algorithms and determined the optimal combination for long-term monitoring. The results indicate that all five algorithms can effectively depict LST spatial distribution under normal temperature conditions. The Jiménez-Muñoz split-window algorithm performs best for small-scale coal fire identification, with a mean absolute error of 3.25 °C and a relative error of 5.53%, and its fitting slope of 0.82 proves superior stability. For high-temperature anomaly extraction methods, the gradient threshold method achieves a 100% overlap rate with actual anomalies and no omission, which is ideal for large-scale surveys; the cluster analysis method balances detection accuracy and economic benefits for pit-scale investigations. Using 52 valid Landsat images from 2013 to 2025, long-term monitoring reveals that high-temperature anomalies are most active in summer, with an average patch area of 5.65 × 10⁵ m², and weaken sharply in winter. According to the observed spatiotemporal evolution patterns, the dynamic changes in thermal anomalies are inferred to be mainly associated with human mining activities, with coal seam conditions as the secondary influencing factor. This study provides reliable technical references for coal mine safety management and coal fire disaster prevention. Full article

High-risk landslide extraction from remote sensing imagery is a fundamental task for geological disaster prevention, emergency response, and land-use planning in mountainous regions. Although deep-learning semantic segmentation has substantially advanced landslide detection from optical imagery, existing methods still suffer from three critical limitations: (i) Interferometric Synthetic Aperture Radar (InSAR) deformation data are treated as auxiliary channels and dominated by optical features during fusion; (ii) predicted masks exhibit fragmented boundaries and incomplete delineation due to the absence of deformation continuity constraints reflecting the physical coherence of slope movements; and (iii) heavy Transformer backbones hinder practical deployment over large areas. To address these issues, we propose a Deformation-Integrity-Guided Symmetric Fusion Network (DIGSFNet) for high-risk landslide extraction from InSAR and optical imagery. The framework consists of three components: a Symmetric Deformation-Aware Encoder (SDAE) that treats InSAR and optical modalities as equal information sources through modality-aware adapters and dynamic sparse cross-modal fusion; a Deformation Integrity Prior Decoder (DIPD) that imposes deformation continuity and boundary-gradient consistency as physical priors to enforce mask completeness and boundary accuracy; and a Lightweight Deployable Student Network (LDSN) obtained via cross-modal knowledge distillation and INT8 quantization for efficient inference. Experiments on the Nanning High-Hazard Landslide Segmentation (Nanning-HHLS) dataset and the public HAEFNet benchmark covering the Qinghai–Tibet–Sichuan landslide-prone regions show that the full DIGSFNet achieves state-of-the-art extraction accuracy, reaching 83.57% and 78.92% mIoU on the two datasets and surpassing the strongest competing method by 2.63 and 3.68 percentage points with a Recall of 91.48% on Nanning-HHLS, while its distilled lightweight student retains 79.24% mIoU at 218 frames per second after INT8 quantization, enabling efficient large-area operational deployment. Full article