Search Results (632)

The coal mining sector in Indonesia faces a high level of risk of disasters; however, to date, there is no specific evaluation framework to measure Emergency Management Capability. This research aims to develop a conceptual model of EMC that applies to the context of the coal mining industry. Using an exploratory qualitative approach, this study employed regulatory analysis and in-depth interviews, which were then thematically analyzed using the NVivo application. The results identified four challenges to EMC implementation, namely the absence of a minimum index standard for assessment, policy and implementation gaps, illegal mining activities, and risk dynamics. In response to these challenges, three strategic approaches were proposed: utilizing the InaRISK platform, adapting the IKD model, and developing standardized EMC instruments. Furthermore, this research formulates seven main components in the mining sector EMC framework, namely (1) risk and threat identification, (2) physical capacity, (3) human resource capacity, (4) prevention, (5) emergency response capability, (6) evaluation and improvement, and (7) recovery and restoration. This framework is expected to serve as a reference for evaluating the preparedness of mining organizations in a systematic, adaptive, and integrated manner within the national safety management system. Full article

The Personal Care Products (PCP) industry, encompassing cosmetics, hygiene, and personal care items, serves millions of consumers daily and operates under constant pressure for innovation, agility, and sustainability. Within this context, supply chains are viewed as complex and integrated systems, composed of interrelated elements whose interactions determine overall performance and are influenced by external factors. Disruptions—particularly those involving indirect suppliers—can propagate throughout the network, affecting operations, reputation, and business outcomes. Despite the importance of the topic, empirical studies that systematically identify and prioritize these risks in the PCP sector remain scarce, which motivated the conduct of this study. Thus, the aim of this research is to identify, analyze, and evaluate the main supply risks faced by the PCP industry, considering severity, occurrence, and detection capability. Methodologically, the research employed an exploratory multi-case design, carried out in three steps: a literature review to identify key supply chain risks; structured interviews with industry experts to analyze and evaluate these risks; and the application of Gray Relational Analysis (GRA) to aggregate expert judgments and construct a prioritized risk ranking. This combination of qualitative and quantitative techniques provided a detailed foundation for analyzing and interpreting the main risks in the Brazilian PCP sector. The results indicate that indirect supplier failure is the most critical risk, prioritized by 70% of the companies studied. Other significant risks include the inability to meet changes in demand, import issues, lack of supply chain visibility, natural and social disasters, and sustainability or reputational concerns. Consequently, this study contributes to a systemic understanding of risk management in the PCP industry supply chain, providing managers with a practical mapping of critical points and highlighting concrete opportunities to strengthen integration, anticipate disruptions, and enhance operational resilience and performance across the sector. Full article

Global Navigation Satellite System (GNSS) meteorology has emerged as a valuable tool for atmospheric monitoring, providing high-resolution, near-real-time data that can significantly improve precipitation nowcasting. This study aims to enhance short-term precipitation forecasting by integrating GNSS-derived Precipitable Water Vapor (PWV)—a key indicator of atmospheric moisture—with traditional meteorological observations. A novel two-step machine learning framework is proposed that combines a Random Forest (RF) model and a Long Short-Term Memory (LSTM) neural network. The RF model first estimates current precipitation based on PWV, surface weather parameters, and auxiliary atmospheric variables. Then, the LSTM network leverages temporal dependencies within the data to predict precipitation for the subsequent hour. This hybrid method capitalizes on the RF’s ability to model complex nonlinear relationships and the LSTM’s strength in handling time series data. The results demonstrate that the proposed approach improves forecasting accuracy, particularly during extreme weather events such as intense rainfall and thunderstorms, outperforming conventional models. By integrating GNSS meteorology with advanced machine learning techniques, this study offers a promising tool for meteorological services, early warning systems, and disaster risk management. The findings highlight the potential of GNSS-based nowcasting for real-time decision-making in weather-sensitive applications. Full article

Aiming at the problems of feature matching difficulty and limited extension application in the existing pixel offset tracking method for large-gradient landslides, this paper proposes an improved pixel offset tracking method based on corner point variation. Taking the Jinshajiang Baige landslide as the research object, the method’s effectiveness is verified using sentinel data. Through a series of experiments, the results show that (1) the use of VV (Vertical-Vertical) and VH (Vertical-Horizontal) polarisation information combined with the mean value calculation method can improve the accuracy and credibility of the circling of the landslide monitoring range, make up for the limitations of the single polarisation information, and capture the landslide range more comprehensively, which provides essential information for landslide monitoring. (2) The choice of scale factor has an essential influence on the results of corner detection, in which the best corner effect is obtained when the scale factor R is 2, which provides an essential reference basis for practical application. (3) By comparing traditional normalized and adaptive window cross-correlation methods with the proposed approach in calculating landslide offset distances, the proposed method shows superior matching accuracy and sliding direction estimation. (4) Analysis of pixels P1, P2, and P3 confirms the method’s high accuracy and reliability in landslide displacement assessment, demonstrating its advantage in tracking pixel offsets in large-gradient scenarios. Therefore, the proposed method offers an effective solution for large-gradient landslide monitoring, overcoming limitations of feature matching and limited applicability. It is expected to provide more reliable technical support for geological disaster management. Full article

The research on river and lake resilience management, ecological restoration, and disaster reduction technologies aims to comprehensively improve the health, stability, and sustainability of aquatic ecosystems. It seeks to reduce the natural disaster risk, promote the sustainable use of water resources, protect biodiversity, strengthen water ecological environment supervision, and advance the widespread practice of the green development concept. This study integrates remote sensing, geographic information system (GIS), and biological slope protection technologies, supported by investigation and geomorphological surveys, to achieve real-time monitoring and data analysis of river and lake ecosystems. Additionally, the application of innovative ecological restoration materials and technologies significantly improves restoration outcomes and operational efficiency. The construction of multi-level wetlands, combined with active community participation, further enhances ecological resilience and stability. Experimental results show that the river and lake resilience management structure increases the strength of slope protection by more than 1.5 times and improves the overall stability by more than 25%. These findings underscore the critical role of integrated ecological and engineering approaches in achieving sustainable development of river and lake ecosystems while effectively reducing the risks of natural disasters. Full article

Precipitation estimation is one of the main inputs of hydrological applications, agriculture, and disaster management, but satellite-based precipitation datasets often present biases and discrepancies compared to ground measurements, particularly for data-scarce regions. The present work discusses the development of a novel methodology that merges quantile mapping with machine learning-based spatial clustering, aiming at enhancing the accuracy and reliability of satellite precipitation data. Results showed that quantile mapping, by aligning the distributional properties of satellite data with in situ measurements, reduced systematic biases. On the other hand, quantile mapping could not capture the extremes in precipitation merely by relying on a simple model complexity–performance trade-off. While increasing the number of clusters enhanced capturing spatial heterogeneity and extreme precipitation events, the benefit from using more clusters was really realized up to a point, as continued improvement in metrics beyond 10 clusters was marginal. Conversely, the extra clusters further did not provide any significant reductions in RMSE or Bias. This showed that the effect of further refinement in model performance showed diminishing returns. This hybrid quantile mapping and clustering framework provides a robust tool that can be adapted for enhancing satellite-based precipitation estimates and therefore has implications for data-poor areas where accurate precipitation information is key to sustainable water resource management, climate-resilient agricultural production, and proactive disaster preparedness that supports long-term environmental and socio-economic sustainability. Full article

Accurate extraction of building features from remotely sensed data is essential for supporting research and applications in urban planning, land management, transportation infrastructure development, and disaster monitoring. Despite the prominence of deep learning as the state-of-the-art (SOTA) methodology for building extraction, substantial challenges remain, largely stemming from the diversity of building structures and the complexity of background features. To mitigate these issues, this study introduces DeepSwinLite, a lightweight architecture based on the Swin Transformer, designed to extract building footprints from very high-resolution (VHR) imagery. The model integrates a novel local-global attention module to enhance the interpretation of objects across varying spatial resolutions and facilitate effective information exchange between different feature abstraction levels. It comprises three modules: multi-scale feature aggregation (MSFA), improving recognition across varying object sizes; multi-level feature pyramid (MLFP), fusing detailed and semantic features; and AuxHead, providing auxiliary supervision to stabilize and enhance learning. Experimental evaluations on the Massachusetts and WHU Building Datasets reveal the superior performance of DeepSwinLite architecture when compared to existing SOTA models. On the Massachusetts dataset, the model attained an OA of 92.54% and an IoU of 77.94%, while on the WHU dataset, it achieved an OA of 98.32% and an IoU of 92.02%. Following the correction of errors identified in the Massachusetts ground truth and iterative enhancement, the model’s performance further improved, reaching 94.63% OA and 79.86% IoU. A key advantage of the DeepSwinLite model is its computational efficiency, requiring fewer floating-point operations (FLOPs) and parameters compared to other SOTA models. This efficiency makes the model particularly suitable for deployment in mobile and resource-constrained systems. Full article

Machine learning techniques have been increasingly used in flood management worldwide to enhance the effectiveness of traditional methods for flood susceptibility mapping. Although these models have achieved higher accuracy than traditional ones, their application has not yet reached full maturity. We focus on applying machine learning models to create flood susceptibility maps (FSMs) for Thessaly, Greece, a flood-prone region with extreme flood events recorded in recent years. This study utilizes 13 explanatory variables derived from topographical, hydrological, hydraulic, environmental and infrastructure data to train the models, using Storm Daniel—one of the most severe recent events in the region—as the primary reference for model training. The most significant of these variables were obtained from satellite data of the affected areas. Four machine learning algorithms were employed in the analysis, i.e., Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF) and eXtreme Gradient Boosting (XGBoost). Accuracy evaluation revealed that tree-based models (RF, XGBoost) outperformed other classifiers. Specifically, the RF model achieved Area Under the Curve (AUC) values of 96.9%, followed by XGBoost, SVM and LR, with 96.8%, 94.0% and 90.7%, respectively. A flood susceptibility map corresponding to a 1000-year return period rainfall scenario at 24 h scale was developed, aiming to support long-term flood risk assessment and planning. The analysis revealed that approximately 20% of the basin is highly prone to flooding. The results demonstrate the potential of machine learning in providing accurate and practical flood risk information to enhance flood management and support decision making for disaster preparedness in the region. Full article

Seismic hazards in Thailand are frequently overlooked in disaster management planning, leading to insufficient research and significant economic losses during earthquake events. The 2014 Chiang Rai earthquake exposed critical vulnerabilities in Thailand’s building practices due to widespread non-compliance with building codes and limited preparedness. This exposure prompted the development of empirical vulnerability functions using loss data from 15,031 damaged residences. The study analyzed government compensation records, which were standardized using replacement cost metrics. Three distinct models were developed through probabilistic and possibilistic modeling approaches. Residual analysis demonstrated the superior performance of the possibilistic approach, with the Possibilistic-based Vulnerability Function achieving a 49.84% reduction in residuals for small loss predictions compared to probability-based models. The research findings indicate that possibility theory—capable of addressing multiple uncertainties—provided a more accurate representation of the observed losses. These results offer valuable guidance for enhancing seismic risk assessment and disaster preparedness strategies in local applications. Full article

Greece faces increasing exposure to natural hazards—particularly wildfires, floods, and earthquakes—driven by climatic, environmental, and spatial factors. This study systematically reviews 108 peer-reviewed publications and official reports, applying PRISMA methodology to evaluate the effectiveness of the national civil protection system. The analysis reveals localized progress, notably in earthquake preparedness due to strict building codes and centralized oversight, but also persistent systemic weaknesses. These include fragmented governance, coordination gaps across agencies, insufficient integration of spatial planning, limited local preparedness, and reactive approaches to disaster management. Case studies of major events, such as the 2018 Mati wildfires and 2023 Thessaly floods, underscore how communication breakdowns and delayed evacuations contribute to substantial human and economic losses. Promising developments—such as SMS-based early warning systems, joint training exercises, and pilot GIS risk-mapping tools—illustrate potential pathways for improvement, though their application remains uneven. Future priorities include strengthening unified command structures, enhancing prevention-oriented planning, investing in interoperable communication systems, and fostering community engagement. The findings position Greece’s civil protection as structurally capable of progress but in need of sustained, systemic reforms to build a resilient, prevention-focused framework for increasing disaster risks. Full article

Xiahe County, in the northwestern Gannan Tibetan Autonomous Prefecture of Gansu Province, faces recurrent geological hazards—including landslides and debris flows. Geological hazards in highly vegetated regions pose severe threats to ecological balance, human settlements, and socio-economic sustainability, hindering the achievement of sustainable development goals (SDGs). Due to the significant topographic relief and high vegetation coverage in this region, traditional manual ground-based surveys face substantial challenges in the investigation and identification of geological hazards, necessitating the adoption of advanced monitoring and identification techniques. This study employs a comprehensive approach integrating optical remote sensing, interferometric synthetic aperture radar (InSAR), and unmanned aerial vehicle (UAV) photogrammetry to investigate and identify geological hazards in the eastern part of Xiahe County, exploring the application capabilities and effectiveness of multisource remote sensing techniques in hazard identification. The results indicate that this study has shortened the time required for on-site investigations by improving the efficiency of disaster identification while also providing comprehensive, multi-angle, and high-precision remote sensing outcomes. These achievements offer robust support for sustainable disaster management and land use planning in ecologically fragile regions. Optical remote sensing, InSAR, and UAV photogrammetry each possess unique advantages and application scopes, but single-technique approaches are insufficient to fully address potential hazard identification. Developing a comprehensive investigation and identification framework that integrates and complements the strengths of multisource technologies has proven to be an effective pathway for the rapid investigation, identification, and evaluation of geological hazards. These results contribute to regional sustainability by enabling targeted risk mitigation, minimizing disaster-induced ecological and economic losses, and enhancing the resilience of vulnerable communities. Full article

UAV delivery and ground transfer scheduling in emergency scenarios represent critical technological systems for enhancing disaster response capabilities and safeguarding lives and property. This study systematically reviews recent advances across eight core research domains: UAV emergency delivery systems, ground–air integrated transportation coordination, emergency logistics optimization, UAV path planning and scheduling algorithms, collaborative optimization between ground vehicles and UAVs, emergency response decision support systems, low-altitude economy and urban air traffic management, and intelligent transportation system integration. Research findings indicate that UAV delivery technologies in emergency contexts have evolved from single-aircraft applications to intelligent multi-modal collaborative systems, demonstrating significant advantages in medical supply distribution, disaster relief, and search-and-rescue operations. Current technological development exhibits four major trends: hybrid optimization algorithms, multi-UAV cooperation, artificial intelligence enhancement, and real-time adaptation capabilities. However, critical challenges persist, including regulatory framework integration, adverse weather adaptability, cybersecurity protection, human–machine interface design, cost–benefit assessment, and standardization deficiencies. Future research should prioritize distributed decision architectures, robustness optimization, cross-domain collaboration mechanisms, emerging technology integration, and practical application validation. This comprehensive review provides systematic theoretical foundations and practical guidance for emergency management agencies in formulating technology development strategies, enterprises in investment planning, and research institutions in determining research priorities. Full article

With the rapid development of urban underground rail transit, its enclosed and densely populated environment significantly increases fire risks, posing serious threats to personnel safety and operational stability. Based on the WSR methodology and 4M theory, this study identifies fire-related factors from the physical, operational, and human dimensions. And refine indicators at the four levels of personnel, equipment and facilities, environment, and management to establish a resilience assessment system for urban underground rail transit fires. The results detailed display the application of Cross-Influence Analysis (CIA) and analytic network process (ANP) methods in fire resilience evaluation, including theoretical framework construction, computational procedures, and result analysis. A comprehensive assessment system is developed, comprising 14 secondary indicators under four primary criteria: resistance capacity, adaptation capacity, absorption capacity, and resilience capacity. And then, the CIA and ANP methods were employed to quantify inter-indicator relationships and weights through 15 expert evaluations and 52 judgment matrices, facilitating disaster-adaptive strategy formulation. Finally, an empirical analysis of Xi’an Metro Line 1 reveals that resistance capacity and resilience capacity are critical to fire resilience, with fire cause investigation and post-incident review exhibiting the highest weights. Meanwhile, resilience enhancement strategies are proposed, including optimized monitoring equipment deployment, strengthened emergency drills, and improved personnel training. The paper innovatively integrates WSR methodology and 4M theory to establish a comprehensive, representative metro fire resilience assessment system with CIA-ANP quantification. This study provides novel methodological support for fire safety assessment in urban underground rail transit systems, offering significant theoretical and practical value. Full article

Environmental monitoring and early disaster prediction require sensor networks that can dynamically reconfigure their operation based on environmental conditions and potential threats. Moving beyond traditional management requires autonomous and adaptive control systems with the ability for intelligent decision-making at the network edge. This paper presents an intelligent agent-based system for autonomous control and optimization of large-scale, distributed electronic sensor networks used for environmental monitoring and disaster prediction. The approach aims at promoting the accuracy and timeliness of disaster prediction by using sensor characteristics knowledge, environmental processes, and network control protocols. The paper presents the architecture and decision-making with potential applications. Full article

Earthquake prediction remains a significant challenge in seismology, and advancements in signal processing techniques have opened new avenues for improving prediction accuracy. This paper explores the application of Time–Frequency Distributions (TFDs) to seismic signals to identify diagnostic precursory patterns of major earthquakes. TFDs provide a comprehensive analysis of the non-stationary nature of seismic data, allowing for the identification of precursory patterns based on energy concentration features. Current earthquake prediction models primarily focus on long-term forecasts, predicting events by identifying a cycle in historical data, or on nowcasting, providing alerts seconds after a quake has begun. However, both approaches offer limited utility for disaster management, compared to short-term earthquake prediction methods. This paper proposes a new possible precursory pattern of major earthquakes, tested through analysis of recent major earthquakes and their respective prior minor earthquakes for five earthquake-prone countries, namely Türkiye, Indonesia, the Philippines, New Zealand, and Japan. Precursors in the time–frequency domain have been consistently identified in all datasets within several hours or a few days before the major earthquakes occurred, which were not present in the observation and analysis of the earthquake catalogs in the time domain. This research contributes towards the ongoing efforts in earthquake prediction, highlighting the potential of quadratic non-linear TFDs as a significant tool for non-stationary seismic signal analysis. To the best of the authors’ knowledge, no similar approach for consistently identifying earthquake diagnostics precursors has been proposed, and, therefore, we propose a novel approach in reliable earthquake prediction using TFD analysis. Full article