Search Results (91)

The need for systems that forecast and respond proactively to meteorological disasters is growing amid climate variability. Although the early warning system in South Korea includes countermeasure information, it remains limited in terms of data recency, granularity, and regional adaptability. Additionally, its closed architecture hinders interoperability with external systems. This study aims to redesign the countermeasure function as an independent cloud-based platform grounded in the common standard terminology framework in South Korea. A multi-dimensional data model was developed using attributes such as crop type, cultivation characteristics, growth stage, disaster type, and risk level. The platform incorporates user-specific customization features and history tracking capabilities, and it is structured using a microservices architecture to ensure modularity and scalability. The proposed system enables real-time management and dissemination of localized countermeasure suggestions tailored to various user types, including central and local governments and farmers. This study offers a practical model for enhancing the precision and applicability of agrometeorological response information. It is expected to serve as a scalable reference platform for future integration with external agricultural information systems. Full article

With climate warming, the global precipitation patterns have undergone significant changes, which will profoundly impact flood–drought disaster regimes and socioeconomic development in key regions of human activity worldwide. The convergence zone of the Indian Ocean monsoon and Pacific monsoon in China covers most of the middle and lower reaches of the Yangtze River (MLRYR), which is located in the transitional area of the second and third steps of China’s terrain. Changes in precipitation patterns in this region will significantly impact flood and drought control in the MLRYR, as well as the socioeconomic development of the MLRYR Economic Belt. In this study, Huaihua area in China was selected as the study area to study the characteristics of regional precipitation change, and to analyze the evolution in the trends in annual precipitation, extreme precipitation events, and their spatiotemporal distribution, so as to provide a reference for the study of precipitation change patterns in the intersection zone. This study utilizes precipitation data from meteorological stations and the China Meteorological Forcing Dataset (CMFD) reanalysis data for the period 1979–2023 in Huaihua region. The spatiotemporal variation in precipitation in the study area was analyzed by using linear regression, the Mann–Kendall trend test, the moving average method, the Mann–Kendall–Sneyers test, wavelet analysis, and R/S analysis. The results demonstrate the following: (1) The annual precipitation in the study area is on the rise as a whole, the climate tendency rate is 9 mm/10 a, and the precipitation fluctuates greatly, showing an alternating change of “dry–wet–dry–wet”. (2) Wavelet analysis reveals that there are 28-year, 9-year, and 4-year main cycles in annual precipitation, and the precipitation patterns at different timescales are different. (3) The results of R/S analysis show that the future precipitation trend will continue to increase, with a strong long-term memory. (4) Extreme precipitation events generally show an upward trend, indicating that their intensity and frequency have increased. (5) Spatial distribution analysis shows that the precipitation in the study area is mainly concentrated in the northeast and south of Jingzhou and Tongdao, and the precipitation level in the west is lower. The comprehensive analysis shows that the annual precipitation in the study area is on the rise and has a certain periodic precipitation law. The spatial distribution is greatly affected by other factors and the distribution is uneven. Extreme precipitation events show an increasing trend, which may lead to increased flood risk in the region and downstream areas. In the future, it is necessary to strengthen countermeasures to reduce the impact of changes in precipitation patterns on local and downstream economic and social activities. Full article

Accurately predicting the drift trajectory of green tides is crucial for assessing potential risks and implementing effective countermeasures. This paper proposes a short-term green-tide drift prediction method that combines green-tide patch characteristics, 1 h interval drift distances from GOCI-II images, and driving-factor data using the XGBoost machine learning model to enhance prediction accuracy. The results demonstrate that the proposed method outperforms the traditional OpenDrift model in short-term predictions. Specifically, at time intervals of 3, 5, and 7 h, the root mean square errors (RMSEs) of the OpenDrift model in the zonal direction are 1.81 km, 2.89 km, and 3.55 km, respectively, whereas the RMSEs of the proposed method are 0.80 km, 0.98 km, and 1.20 km, respectively; in the meridional direction, the RMSEs of the OpenDrift model are 1.77 km, 2.67 km, and 3.10 km, while the RMSEs for the proposed method are 0.82 km, 1.10 km, and 1.25 km, respectively. Furthermore, the proposed XGBoost method more-accurately tracks the actual positions of green-tide patches compared to the OpenDrift model. Specifically, at the 25 h interval, the proposed method continues to accurately predict patch positions, while the OpenDrift model exhibits significant deviations. This study demonstrates that the proposed method, by learning drift patterns from historical data, effectively predicts the short-term drift process of green tides. It provides valuable support for early warning systems, thereby helping to mitigate the ecological and economic impacts of green-tide disasters. Full article

The length of the mainland coastline in Guangdong Province ranks first in the country, and the rapid development of the marine economy have also supported Guangdong Province’s GDP to remain at the top of the country for 35 consecutive years. The coastline has extremely important ecological functions and resource values. Guangdong Province has always attached great importance to the renovation and restoration of its coastline, continuously strengthening the ecological, disaster reduction, and tourism functions of the coastal areas. This article analyzes the main measures, achievements, and main problems of coastal protection in Guangdong Province and selects typical areas for driving force analysis. Finally, some thoughts and targeted countermeasures on the protection of Guangdong Province’s coastline are proposed, which provide useful references for comprehensively strengthening coastline protection, scientifically carrying out coastline renovation and restoration, and improving the natural coastline retention rate in the future. This can also output wisdom and experience for the construction of a maritime power under the background of land–sea coordination. Full article

Grain supply chains remain stable in the face of natural disasters, and the resilience of the grain supply chain plays an important role. In a complex scenario of exposure to shocks, it is significant to identify the critical nodes of the grain supply chain and propose countermeasures accordingly to enhance the resilience of the grain supply chain. In this paper’s study, firstly, a triangular model of contradictory events is used to describe complex scenarios and obtain Bayesian network nodes. Secondly, the fragmentation of the scenario is based on the description of the scene, the scene stream is constructed, the event network is obtained, and the Bayesian network structure is built on the basis. Then, combining expert knowledge and D–S evidence theory, the Bayesian network parameters are determined, and the Bayesian network model is built. Finally, the key nodes of the grain supply chain are identified in the context of the 2022 drought data in the Yangtze River Basin in China, and, accordingly, a strategy for improving the resilience of the grain supply chain is proposed in stages. This study provides a new research perspective on issues related to grain supply-chain resilience and enriches the theoretical foundation of research related to supply-chain resilience. Full article

China’s Dongting Lake area is intertwined with rivers and lakes and possesses many water systems. As such, it is one of the most complicated areas in the Yangtze River Basin, in terms of the complexity of its flood control. Over time, siltation and reclamation in the lake area have greatly weakened the river discharge capacity of the lake area, and whether it can endure extreme floods remains an open question. As there is no effective scenario simulation model for the lake area, this study constructs a hydrological model for the Jingjiang–Dongting Lake system and verifies the model using data from 11 typical floods occurring from 1954 to 2020. The parameters derived from 2020 data reflect the latest hydrological relationship between the lake and the river, while meteorological data from 1954 and 1998 are used as inputs for various scenarios with the aim of evaluating the flood pressure of the lake area, using the water levels at the Chengglingji and Luoshan stations as indicators. The preliminary results demonstrate that the operation of the upstream Three Gorges Dam and flood storage areas cannot completely offset the flood pressure faced by the lake area. Therefore, the reinforcement and raising of embankments should be carried out, in order to cope with potential extreme flood events. The methodology and results of this study have reference value for policy formation, flood control, and assessment and dispatching in similar areas. Full article

Family reserves are an important part of national reserves, and how to do a good job in family reserves is a common concern of the government and society. Under the non-normal conditions of major accidents and disasters, wars, plagues, social unrest, etc., urban food supply mainly depends on external supply guarantee, and urban residents’ risk perception is more sensitive. Based on the Theory of Planned Behavior and Norm Activation Model, this study constructs an analytical framework for the risk perception, perceived behavior control, and family reserve efficacy of urban residents under non-normal conditions from the perspectives of rationality and sensibility, self-interest, and altruism. The perceived behavior control of household food reserves in non-normal conditions is affected by risk perception, subjective norms, and personal norms. On this basis, countermeasures and suggestions are put forward: Urban residents should strengthen their sense of risk and responsibility for storing food at home, reserve food appropriately, and develop a good habit of family saving. On the other hand, it is necessary to pay attention to personal norms, reduce the negative impact of subjective norms on residents, and avoid excessive food storage and food waste. Full article

The objective of this study is to investigate the potential risks posed by snowdrifts, a prevalent cause of natural disasters in northern China, on railway subgrades, and to assess their risk level. As a wind-driven process of snow migration and redeposition, snowdrifts pose a significant threat to the safety of transportation infrastructures. This study focuses on the Afu Railway in Xinjiang, situated on the northern slopes of the Eastern Tianshan Mountains, where it experiences periodic snowdrifts. We employed a combination of the Analytic Hierarchy Process (AHP) and fuzzy comprehensive evaluation (FCE) to construct an integrated evaluation system for assessing the risk of snowdrift to railway subgrades. The results indicate that subgrade design parameters and regional snowfield conditions are two key metrics affecting the extent of snowdrift disasters, with topography, vegetation coverage, and wind speed also exerting certain impacts. The evaluation method of this study aligns with the results of on-site observations, verifying its accuracy and practicality, thereby providing a solid risk assessment framework for snowdrifts along the railway. The scientific and systematic hazard assessment method of railway subgrades developed in this research provides basic data and theoretical support for future research, and provides a scientific basis for relevant departments to formulate countermeasures, so as to improve the safety and reliability of railway operations. Full article

The China–Russia crude oil pipeline (CRCOP) operates at a temperature that continuously thaws the surrounding permafrost, leading to secondary periglacial phenomena along the route. However, the evolution and formation mechanisms of these phenomena are still largely unknown. We used multi-temporal airborne light detection and ranging (LiDAR), geophysical, and field observation data to quantify the scale of ponding and icing, capture their dynamic development process, and reveal their development mechanisms. The results show that the average depth of ponding within 5 m on both sides of the pipeline was about 31 cm. The volumes of three icings (A–C) above the pipeline were 133 m³, 440 m³, and 186 m³, respectively. Icing development can be divided into six stages: pipe trench settlement, water accumulation in the pipe trench, ponding pressure caused by water surface freezing, the formation of ice cracks, water overflow, and icing. This study revealed the advantages of airborne LiDAR in monitoring the evolution of periglacial phenomena and provided a new insight on the development mechanisms of the phenomena by combining LiDAR with geophysics and field observation. The results of our study are of great significance for developing disaster countermeasures and ensuring the safe operation of buried pipelines. Full article

In recent years, the collaborative operation of multiple unmanned aerial vehicles (UAVs) has been an important advancement in drone technology. The research on multi-UAV collaborative flight path planning has garnered widespread attention in the drone field, demonstrating unique advantages in complex task execution, large-scale monitoring, and disaster response. As one of the core technologies of multi-UAV collaborative operations, the research and technological progress in trajectory planning algorithms directly impact the efficiency and safety of UAV collaborative operations. This paper first reviews the application and research progress of path-planning algorithms based on centralized and distributed control, as well as heuristic algorithms in multi-UAV collaborative trajectory planning. It then summarizes the main technical challenges in multi-UAV path planning and proposes countermeasures for multi-UAV collaborative planning in government, business, and academia. Finally, it looks to future research directions, providing ideas for subsequent studies in multi-UAV collaborative trajectory planning technology. Full article

With the development of computer technology, landslide recognition based on machine learning methods has been widely applied in geological disaster management and research. However, in landslide identification, the problems of an insufficient number of samples and an imbalance of samples are often ignored; that is, landslide samples are much smaller than non-landslide samples. In order to solve this problem, taking the main urban area of Lanzhou City as an example, this paper proposes to construct a semi-supervised generated countermeasure network (SSGAN) model, which aims to achieve high performance with a limited number of labeled samples for precise landslide identification, and to help prevent and reduce the harm caused by disasters. In order to express the environmental characteristics of landslide development and the optical texture features of landslide occurrence, the study constructs three sets of samples to represent landslide features, including a landslide influencing factor sample set, a Sentinel-2A optical remote sensing sample set, a joint influencing factor and Sentinel-2A sample set. The three kinds of sample sets are transferred to SSGAN for training to form a comparative study. The results show that the joint sample set has excellent feature results in discriminator and generator. Through the experimental comparison, the model proposed in this paper is compared with the model without semi-supervised generated confrontation training. The experimental results show that the proposed method is better than the unsupervised adversarial learning model in terms of accuracy, F1 score, Kappa coefficient, and MIoU. A total of 160 landslides have been identified in the study area, with a total area of 10.328 km², with an accuracy rate of 83%. Therefore, the generated results are accurate and reliable, and show that SSGAN can better distinguish landslides from non-landslides in an image, under the condition of obtaining a large number of unmarked environmental features; enhance the effect of landslide classification in complex geographical environment; and then put forward effective suggestions for the prevention and control of landslides and geological disasters in the main urban area of Lanzhou. Full article

The Polar Operational Limit Assessment Risk Indexing System (POLARIS) has been established as a viable framework for assessing operational capabilities and associated risks in polar waters. Despite its inherent suitability for high-latitude territories, ships navigating through seasonal ice-infested waters at lower latitudes also encounter critical safety, environmental, and economic issues exacerbated by the presence of ice. This necessitates a reliable and adaptable methodology that can serve as a reference for devising effective countermeasures. This study evaluated the use of POLARIS in the intricate ice conditions prevalent in the northern navigable waters (channels and anchorages) within Liaodong Bay of the Bohai Sea, located at relatively low latitudes. Using GF-4 satellite imagery, ice conditions were collected, and the POLARIS methodology was employed to calculate Risk Index Outcome (RIO) values for non-ice-strengthened vessels during the winter season of 2021–2022. The results showed that sectors 3, 4, 5, 7, 9, 10, and 11 within the northern part of Liaodong Bay exhibited a higher risk, with sectors 5 and 10 exhibiting the most significant risk, while sectors 1 and 2 demonstrated relatively lower risk levels. The concurrence of these findings with acknowledged ice patterns and local maritime practices confirms the applicability of the POLARIS methodology in saline, seasonally ice-covered seas. Notably, the combination of POLARIS with high-resolution satellite imagery facilitated a more precise and rapid assessment of ice risk, thereby enhancing situational awareness and informing decision-making processes in maritime operations under icy conditions. In addition, this study provides preliminary evidence that POLARIS is suitable for fine-scale scenarios, in addition to being applicable to sparse-scale scenarios, such as polar waters, especially with high-resolution ice data. At the same time, this study highlights the potential of POLARIS as a disaster prevention strategy and a tool for the maritime industry to address ice challenges. Full article

In Japan, the heavy rain disaster that occurred in July 2018 revealed that about 70% of the streams affected by debris flows that resulted in human casualties were small, steep mountain streams with a catchment area < 0.05 km². Generally, many streams that are close to residential houses or roads do not have a constant flow of water and are known to pose a high risk of human fatalities when a debris flow occurs. This study aimed to promote sediment control as debris flow countermeasures in non-flowing mountain streams, utilizing secondary manufactured products (permeable debris flow barriers) with excellent constructability, focusing on the mechanism of sediment outflow from the gaps between a permeable debris flow barrier and mountain stream side banks. The necessity and effectiveness of preventative measures based on preliminary experimental results are presented. When impermeable structures were installed at both ends of the permeable debris flow barrier side, compared to using only a permeable debris flow barrier (covering the entire width with permeable debris flow barriers), we found that the capture function improved significantly, achieving a 200% increase in effectiveness. Full article

Strong winds, particularly in the absence of disaster-resistant designs, significantly impact the stability of greenhouse foundations and eventually lead to structural damage and potential harm to crops. As a countermeasure, rebar stakes are commonly used to reinforce the foundations of non-disaster-resistant greenhouses. This study evaluates the pull-out resistance (R_pull-out) of rebar stakes considering various factors like soil compaction, embedded length, installation duration and angle, and changes in soil water content against uplift pressure by strong winds. A combination of field (i.e., the cone penetration test and rebar stake pull-out test) and laboratory (i.e., the compaction test, soil compaction meter test, and soil box test) tests are performed for the assessment of R_pull-out. The results indicate that R_pull-out increases with higher soil compaction, greater embedded length, longer installation duration, and an inclined installation angle. The soil compaction exerts the most significant impact; 90% to 100% of the soil compaction rate has approximately 10 folds higher R_pull-out than the 60–70% compaction rate. If the embedded length is increased from 20 cm to 40 cm, there is a two-fold increase in the average of R_pull-out. Inclined installation of rebar stakes increases R_pull-out by 250% to 350% compared to vertical installation, and rebar stakes installed prior to the uplift event have 1.5 to 6.4 fold increases in R_pull-out than those with instant installation. Additionally, we observed variations in the surface soil moisture due to climatic changes introducing variability in R_pull-out. These findings lead to the proposition of efficient rebar stake installation methods, contributing to the enhanced stability of a greenhouse. Full article

Meteorological disasters on highways can significantly reduce road traffic efficiency. Low visibility caused by dense fog is a severe meteorological disaster that greatly increases the incidence of traffic accidents on highways. Accurately predicting highway visibility and taking timely countermeasures can mitigate the impact of meteorological disasters and enhance traffic safety. This paper introduces the ATCNet model for highway visibility prediction. In ATCNet, we integrate Transformer, Capsule Networks (CapsNet), and self-attention mechanisms to leverage their respective complementary strengths. The Transformer component effectively captures the temporal characteristics of the data, while the Capsule Network efficiently decodes the spatial correlations and hierarchical structures among multidimensional meteorological elements. The self-attention mechanism, serving as the final decision-refining step, ensures that all key temporal and spatial hierarchical information is fully considered, significantly enhancing the accuracy and reliability of the predictions. This integrated approach is crucial in understanding highway visibility prediction tasks influenced by temporal variations and spatial complexities. Additionally, this study provides a self-collected publicly available dataset, WD13VIS, for meteorological research related to highway traffic in high-altitude mountain areas. This study evaluates the model’s performance in terms of Mean Squared Error (MSE) and Mean Absolute Error (MAE). Experimental results show that our ATCNet reduces the MSE and MAE by 1.21% and 3.7% on the WD13VIS dataset compared to the latest time series prediction model architecture. On the comparative dataset WDVigoVis, our ATCNet reduces the MSE and MAE by 2.05% and 5.4%, respectively. Our model’s predictions are accurate and effective, and our model shows significant progress compared to competing models, demonstrating strong universality. This model has been integrated into practical systems and has achieved positive results. Full article