Search Results (11)

In high-mountain canyon areas, complex geological environments lead to frequent cascading disasters with unclear triggering mechanisms, posing severe threats to human life and property. Existing knowledge graph research in geology predominantly focuses on single-hazard types or general geological entities, lacking structured modeling and specialized datasets for cascading disaster processes, particularly the evolutionary chains in high-mountain canyon settings. To address this gap, this study proposes a method for constructing a knowledge graph tailored to cascading disasters in high-mountain canyon regions. First, a three-layer schema framework—comprising concept, relation, and instance layers—was designed to systematically characterize the knowledge elements and evolutionary relationships of disaster chains. To address the lack of a knowledge dataset for cascade disasters, this paper integrates multi-source heterogeneous data to construct a high-mountain canyon cascading disasters entity–relation dataset (DCER-MC), providing a reliable benchmark for related tasks. Based on this dataset, we implemented the knowledge graph and conducted disaster chain analysis. Experiments and applications demonstrate that the constructed knowledge graph effectively supports structured storage, centralized management, and scenario-based application of regional cascading disaster information. The main contributions of this work are (1) proposing a targeted schema framework for cascading-disaster knowledge graphs; (2) releasing a specialized dataset for cascading disasters in high-mountain canyon regions; and (3) establishing a complete pipeline from data to knowledge to scenario-based services, offering a novel knowledge-driven paradigm for disaster chain risk identification, inference prediction, and emergency decision-making in these areas. Full article

Ports face critical security threats from hazardous cargo misdeclaration, which poses unique challenges due to its high concealment and catastrophic potential, as exemplified by the Beirut Port explosion. Traditional resilience assessment approaches relying on hazard state transition probabilities require abundant historical data or extensive domain expertise for probability elicitation, and static indicator-based assessment frameworks fail to capture the spatiotemporal evolution characteristics of disasters. To address these challenges, this study proposes a hybrid framework that leverages the Large Language Model (LLM)’s generalizable world knowledge for data augmentation while developing a Spatiotemporal Graph Neural Network (STGNN) to predict dynamic disaster propagation. Specifically, a multimodal LLM is employed to extract structured port state descriptions from temporally aligned disaster data and infer the states at undocumented time steps. With more disaster scenarios adapted from the real cases using the LLM, a STGNN is trained to learn the disaster evolution dynamics and make efficient real-time inference for resilience assessment and intervention strategy evaluation. Validation on Tianjin and Beirut Port incidents demonstrates that the framework accurately predicts disaster propagation pathways and identifies critical intervention priorities. It also reveals that topology-based intervention strategies substantially accelerate recovery, while adverse environmental conditions significantly amplify cumulative functional loss. This study represents an advancement toward AI-driven resilience modeling, offering port operators and regulators an adaptable, scalable decision support tool for intelligent safety governance. Full article

Amid the increasing frequency and severity of major disasters, the rapid spread of online misinformation poses substantial risks to public safety, effective crisis management, and long-term societal sustainability. Current methods for managing disaster-related rumors rely on static, rule-based approaches that lack scalability, fail to capture nuanced misinformation, and are limited to reactive responses, hindering effective disaster management. To address this gap, this study proposes a novel framework that leverages large language models (LLMs) and event knowledge graphs (EKGs) to facilitate the sustainable agile identification and adaptive control of disaster-related online rumors. The framework follows a multi-stage process, which includes the collection and preprocessing of disaster-related online data, the application of Gaussian Mixture Wasserstein Autoencoders (GMWAEs) for sentiment and rumor analysis, and the development of EKGs to enrich the understanding and reasoning of disaster events. Additionally, an enhanced model for rumor identification and risk control is introduced, utilizing Graph Attention Networks (GATs) to extract node features for accurate rumor detection and prediction of rumor propagation paths. Extensive experimental validation confirms the efficacy of the proposed methodology in improving disaster response. This study contributes novel theoretical insights and presents practical, scalable solutions for rumor control and risk management during crises. Full article

Meteorological and climatological trends are surely changing the way urban infrastructure systems need to be operated and maintained. Urban road traffic fluctuates more significantly under the interference of strong wind–rain weather, especially during tropical cyclones. Deep learning-based methods have significantly improved the accuracy of traffic prediction under extreme weather, but their robustness still has much room for improvement. As the frequency of extreme weather events increases due to climate change, accurately predicting spatiotemporal patterns of urban road traffic is crucial for a resilient transportation system. The compounding effects of the hazards, environments, and urban road network determine the spatiotemporal distribution of urban road traffic during an extreme weather event. In this paper, a novel Knowledge-driven Attribute-Augmented Attention Spatiotemporal Graph Convolutional Network (KA3STGCN) framework is proposed to predict urban road traffic under compound hazards. We design a disaster-knowledge attribute-augmented unit to enhance the model’s ability to perceive real-time hazard intensity and road vulnerability. The attribute-augmented unit includes the dynamic hazard attributes and static environment attributes besides the road traffic information. In addition, we improve feature extraction by combining Graph Convolutional Network, Gated Recurrent Unit, and the attention mechanism. A real-world dataset in Shenzhen City, China, was employed to validate the proposed framework. The findings show that the prediction accuracy of traffic speed can be significantly increased by 12.16%~31.67% with disaster information supplemented, and the framework performs robustly on different road vulnerabilities and hazard intensities. The framework can be migrated to other regions and disaster scenarios in order to strengthen city resilience. Full article

Accurate prediction of key ocean elements (e.g., chlorophyll-a concentration, sea surface temperature, etc.) is imperative for maintaining marine ecological balance, responding to marine disaster pollution, and promoting the sustainable use of marine resources. Existing spatio-temporal prediction models primarily rely on either physical or data-driven approaches. Physical models are constrained by modeling complexity and parameterization errors, while data-driven models lack interpretability and depend on high-quality data. To address these challenges, this study proposes OKG-ConvGRU, a domain knowledge-guided remote sensing prediction framework for ocean elements. This framework integrates knowledge graphs with the ConvGRU network, leveraging prior knowledge from marine science to enhance the prediction performance of ocean elements in remotely sensed images. Firstly, we construct a spatio-temporal knowledge graph for ocean elements (OKG), followed by semantic embedding representation for its spatial and temporal dimensions. Subsequently, a cross-attention-based feature fusion module (CAFM) is designed to efficiently integrate spatio-temporal multimodal features. Finally, these fused features are incorporated into an enhanced ConvGRU network. For multi-step prediction, we adopt a Seq2Seq architecture combined with a multi-step rolling strategy. Prediction experiments for chlorophyll-a concentration in the eastern seas of China validate the effectiveness of the proposed framework. The results show that, compared to baseline models, OKG-ConvGRU exhibits significant advantages in prediction accuracy, long-term stability, data utilization efficiency, and robustness. This study provides a scientific foundation and technical support for the precise monitoring and sustainable development of marine ecological environments. Full article

To effectively mitigate service slope disaster risks in mountainous areas and enhance the overall safety of highway operations, based on the geological and structural characteristics of slopes, considering slope technical conditions, overall stability, and potential disaster consequences, 25 important influencing factors are systematically identified. The identification process integrates insights from the relevant literature, expert opinions, and historical disaster maintenance records of such slopes. An integrated approach combining Interpretive Structural Modeling (ISM) and Bayesian Networks (BNs) is utilized to conduct a quantitative analysis of the interrelationships and impact strength of factors influencing the disaster risk of mountainous service highway slopes. The aim is to reveal the causal mechanism of slope disaster risk and provide a scientific basis for risk assessment and prevention strategies. Firstly, the relationship matrix is constructed based on the relevant prior knowledge. Then, the reachability matrix is computed and partitioned into different levels to form a directed graph from which the Bayesian network structure is constructed. Subsequently, the expert’s subjective judgment is further transformed into a set of prior and conditional probabilities embedded in the BN to perform causal inference to predict the probability of risk occurrence. Real-time diagnosis of disaster risk triggers operating slopes using backward reasoning, sensitivity analysis, and strength of influence analysis capabilities. As an example, the earth excavation slope in the mountainous area of Anhui Province is analyzed using the established model. The results showed that the constructed slope failure risk model for mountainous operating highways has good applicability, and the possibility of medium slope failure risk is high with a probability of 34%, where engineering geological conditions, micro-topographic landforms, and the lowest monthly average temperature are the main influencing factors of slope hazard risk for them. The study not only helps deepen the understanding of the evolutionary mechanisms of slope disaster risk but also provides theoretical support and practical guidance for the safe operation and disaster prevention of mountainous highways. The model offers clear risk information, serving as a scientific basis for managing service slope disaster risks. Consequently, it effectively reduces the likelihood of slope disasters and enhances the safety of highway operation. Full article

The increasing frequency and magnitude of landslides underscore the growing importance of landslide prediction in light of factors like climate change. Traditional methods, including physics-based methods and empirical methods, are beset by high costs and a reliance on expert knowledge. With the advancement of remote sensing and machine learning, data-driven methods have emerged as the mainstream in landslide prediction. Despite their strong generalization capabilities and efficiency, data-driven methods suffer from the loss of semantic information during training due to their reliance on a ‘sequence’ modeling method for landslide scenarios, which impacts their predictive accuracy. An innovative method for landslide prediction is proposed in this paper. In this paper, we propose an innovative landslide prediction method. This method designs the NADE ontology as the schema layer and constructs the data layer of the knowledge graph, utilizing tile lists, landslide inventory, and environmental data to enhance the representation of complex landslide scenarios. Furthermore, the transformation of the landslide prediction task into a link prediction task is carried out, and a knowledge graph embedding model is trained to achieve landslide predictions. Experimental results demonstrate that the method improves the F1 score by 5% in scenarios with complete datasets and 17% in scenarios with sparse datasets compared to data-driven methods. Additionally, the application of the knowledge graph embedding model is utilized to generate susceptibility maps, and an analysis of the effectiveness of entity embeddings is conducted, highlighting the potential of knowledge graph embeddings in disaster management. Full article

During the volatile market period of 2019–2021, characterized by geopolitical shifts, economic sanctions, pandemics, natural disasters, and wars, the global market presented a complex landscape for financial decision making and motivated this study. This study makes two groundbreaking and novel contributions. First, we augment Plunket’s emotional research and leverage the emotional classification algorithm in Fin-Emotion to introduce a novel quantitative metric, “emotion magnitude”, that captures the emotional undercurrents of the market. When integrated with traditional time series analysis using Temporal Convolutional Networks applied to stock market futures, this metric offers a more holistic understanding of market dynamics. In our experiments, incorporating it as a feature led to significantly better performance on both the training and validation sets (9.26%, 52.11%) compared to traditional market-based risk measures, in predicting futures market trends based on the commodities and supply chains analyzed. Second, we deploy a multidimensional data science framework that synthesizes disparate data streams and analyses. This includes stock metrics of sector-leading companies, the time horizon of significant market events identified based on company stock data, and the extraction of further knowledge concepts identified through “emotion magnitude” analysis. Our approach stitches together countries, commodities, and supply chains identified in the targeted news search and identifies the domestic companies impacted based on the time horizon of these emotional supply chain events. This methodology culminates in a unified knowledge graph that not only highlights the relationships between supply chain disruptions, affected corporations, and commodities but also quantifies the broader systemic implications of such market events that are revealed. Collectively, these innovations form a robust analytical tool for financial risk strategy, empowering stakeholders to navigate an ever-evolving financial global ecosystem with enhanced insights. This graph encapsulates multi-dimensional forces and enables stakeholders to anticipate and understand the broader causal implications of related supply chain and market events (such as economic sanctions’ impact on the energy, technology, and telecommunication sectors). Full article

Forest fires destroy the ecological environment and cause large property loss. There is much research in the field of geographic information that revolves around forest fires. The traditional forest fire prediction methods hardly consider multi-source data fusion. Therefore, the forest fire predictions ignore the complex dependencies and correlations of the spatiotemporal kind that usually bring valuable information for the predictions. Although the knowledge graph methods have been used to model the forest fires data, they mainly rely on artificially defined inference rules to make predictions. There is currently a lack of a representation and reasoning methods for forest fire knowledge graphs. We propose a knowledge-graph- and representation-learning-based forest fire prediction method in this paper for addressing the issues. First, we designed a schema for the forest fire knowledge graph to fuse multi-source data, including time, space, and influencing factors. Then, we propose a method, RotateS2F, to learn vector-based knowledge graph representations of the forest fires. We finally leverage a link prediction algorithm to predict the forest fire burning area. We performed an experiment on the Montesinho Natural Park forest fire dataset, which contains 517 fires. The results show that our method reduces mean absolute deviation by 28.61% and root-mean-square error by 53.62% compared with the previous methods. Full article

Floods are the most frequent and highest-impact among the natural disasters caused by global climate change. A large number of flood disaster knowledge were buried in the scientific literature. This study mines research trends and hotspots on flood disasters and identifies their quantitative and spatial distribution features using natural language process technology. The abstracts of 14,076 studies related to flood disasters from 1990 to 2020 were used for text mining. The study used logistic regression to classify themes, adopted the dictionary matching method to analyze flood disaster subcategories, analyzed the spatial distribution characteristics of research institutions, and used Stanford named entity recognition to identify hot research areas. Finally, the disaster information was integrated and visualized as a knowledge graph. The main findings are as follows. (1) The research hotspots are concentrated on flood disaster risks and prediction. Rainfall, coastal floods, and flash floods are the most-studied flood disaster sub-categories. (2) There are some connections and differences between the physical occurrence and research frequency of flood disasters. Occurrence frequency and research frequency of flood disasters are correlated. However, the spatial distribution at the global and intercontinental scales is geographically imbalanced. (3) The study’s flood disaster knowledge graph contains 39,679 nodes and 64,908 edges, reflecting the literature distribution and field information on the research themes. Future research will extract more disaster information from the full texts of the studies to enrich the flood disaster knowledge graph and obtain more knowledge on flood disaster risk and reduction. Full article

Natural disasters have frequently occurred and caused great harm. Although the remote sensing technology can effectively provide disaster data, it still needs to consider the relevant information from multiple aspects for disaster analysis. It is hard to build an analysis model that can integrate the remote sensing and the large-scale relevant information, particularly at the sematic level. This paper proposes a disaster prediction knowledge graph for disaster prediction by integrating remote sensing information, relevant geographic information, with the expert knowledge in the field of disaster analysis. This paper constructs the conceptual layer and instance layer of the knowledge graph by building a common semantic ontology of disasters and a unified spatio-temporal framework benchmark. Moreover, this paper represents the disaster prediction model in the forms of knowledge of disaster prediction. This paper demonstrates experiments and cases studies regarding the forest fire and geological landslide risk. These investigations show that the proposed method is beneficial to multi-source spatio-temporal information integration and disaster prediction. Full article