Search Results (2,165)

In recent years, geographic knowledge graphs (GeoKGs) have shown great promise in representing spatio-temporal and event-driven knowledge. However, existing knowledge graph embedding approaches mainly focus on structural patterns and often overlook the dynamic evolution of entities in both time and space, which limits their effectiveness in downstream reasoning tasks. To address this, we propose a spatio-temporal evolutionary knowledge embedding approach (ST-EKA) that enhances entity representations by modeling their evolution through type-aware encoding, temporal and spatial decay mechanisms, and context aggregation. ST-EKA integrates four core components, including an entity encoder constrained by relational type consistency, a temporal encoder capable of handling both time points and intervals through unified sampling and feedforward encoding, a multi-scale spatial encoder that combines geometric coordinates with semantic attributes, and an evolutionary knowledge encoder that employs attention-based spatio-temporal weighting to capture contextual dynamics. We evaluate ST-EKA on three representative GeoKG datasets—GDELT, ICEWS, and HAD. The results demonstrate that ST-EKA achieves an average improvement of 6.5774% in AUC and 5.0992% in APR on representation learning tasks. In question answering tasks, it yields a maximum average increase of 1.7907% in AUC and 0.5843% in APR. Notably, it exhibits superior performance in chain queries and complex spatio-temporal reasoning, validating its strong robustness, good interpretability, and practical application value. Full article

Falls are a major safety concern in physically demanding occupations such as roofing, where workers operate on inclined surfaces under unstable postures. While inertial measurement units (IMUs) are widely used in wearable fall detection systems, they often fail to capture early indicators of instability related to foot–ground interactions. This study evaluates the effectiveness of plantar pressure sensors, alone and combined with IMUs, for fall detection on sloped surfaces. We collected data in a controlled laboratory environment using a custom-built roof mockup with incline angles of 0°, 15°, and 30°. Participants performed roofing-relevant activities, including standing, walking, stooping, kneeling, and simulated fall events. Statistical features were extracted from synchronized IMU and plantar pressure data, and multiple machine learning models were trained and evaluated, including traditional classifiers and deep learning architectures, such as MLP and CNN. Our results show that integrating plantar pressure sensors significantly improves fall detection. A CNN using just three IMUs and two plantar pressure sensors achieved the highest F1 score of 0.88, outperforming the full 17-sensor IMU setup. These findings support the use of multimodal sensor fusion for developing efficient and accurate wearable systems for fall detection and physical health monitoring. Full article

Collaborative business ecosystems (CBEs) are increasingly exposed to disruptive events (e.g., pandemics, supply chain breakdowns, cyberattacks) that challenge organizational adaptability and value creation. Traditional approaches to resilience and robustness often fail to capture the full range of systemic responses. This study introduces a unified mathematical framework to evaluate four crisis response modes—plasticity, resilience, transformative resilience, and antifragility—within complex adaptive networks. Grounded in complex systems and collaborative network theory, our model formalizes both internal organizational capabilities (e.g., adaptability, learning, innovation, structural flexibility) and strategic interventions (e.g., optionality, buffering, information sharing, fault-injection protocols), linking them to pre- and post-crisis performance via dynamic adjustment functions. A composite performance score is defined across four dimensions (Innovation, Contribution, Prestige, and Responsiveness to Business Opportunities), using capability–strategy interaction matrices, weighted performance change functions, and structural transformation modifiers. The sensitivity analysis and scenario simulations enable a comparative evaluation of organizational configurations, strategy impacts, and phase-transition thresholds under crisis. This indicator-based formulation provides a quantitative bridge between resilience theory and practice, facilitating evidence-based crisis management in networked business environments. Full article

Multi-source fusion techniques have emerged as cutting-edge approaches for spatial precipitation estimation, yet they face persistent accuracy limitations, particularly under extreme conditions. Machine learning offers new opportunities to improve the precision of these estimates. To bridge this gap, we propose a hybrid artificial neural network–geographically weighted regression (ANN–GWR) model that synergizes event recognition and quantitative estimation. The ANN module dynamically identifies precipitation events through nonlinear pattern learning, while the GWR module captures location-specific relationships between multi-source data for calibrated rainfall quantification. Validated against 60-year historical data (1960–2020) from China’s Yongding River Basin, the model demonstrates superior performance through multi-criteria evaluation. Key results reveal the following: (1) the ANN-driven event detection achieves 10% higher accuracy than GWR, with a 15% enhancement for heavy precipitation events (>50 mm/day) during summer monsoons; (2) the integrated framework improves overall fusion accuracy by more than 10% compared to conventional GWR. This study advances precipitation estimation by introducing an artificial neural network into the event recognition period. Full article

Selecting an appropriate model for discharge simulation remains a fundamental challenge in modeling. While artificial neural networks (ANNs) have been widely accepted due to detecting streamflow patterns, they require large datasets for efficient training. However, when short-term datasets are available, training ANNs becomes problematic. Autoregressive integrated moving average (ARIMA) models offer a promising alternative; however, severe volatility, nonlinearity, and trends in hydrological time series can still lead to significant errors. To address these challenges, this study introduces a new adaptive hybrid model, ARIMA-iGARCH, designed to account volatility, variance inconsistency, and nonlinear behavior in short-term hydrological datasets. We apply the model to four hourly discharge time series from the Schwarzbach River at the Nauheim gauge in Hesse, Germany, under the assumption of normally distributed residuals. The results demonstrate that the specialized parameter estimation method achieves lower complexity and higher accuracy. For the four events analyzed, R² values reached 0.99, 0.96, 0.99, and 0.98; RMSE values were 0.031, 0.091, 0.023, and 0.052. By delivering accurate short-term discharge predictions, the ARIMA-iGARCH model provides a basis for enhancing water resource planning and flood risk management. Overall, the model significantly improves modeling long memory, nonlinear, nonstationary shifts in short-term hydrological datasets by effectively capturing fluctuations in variance. Full article

This study analyzes air pollution time-series big data to assess stationarity, seasonal patterns, and the performance of machine learning models in forecasting PM2.5 concentrations. Fifty-two low-cost sensors (LCS) were deployed across Krakow city and its surroundings (Poland), collecting hourly air quality data and generating nearly 20,000 observations per month. The network captured both spatial and temporal variability. The Kwiatkowski–Phillips–Schmidt–Shin (KPSS) test confirmed trend-based non-stationarity, which was addressed through differencing, revealing distinct daily and 12 h cycles linked to traffic and temperature variations. Additive seasonal decomposition exhibited time-inconsistent residuals, leading to the adoption of multiplicative decomposition, which better captured pollution outliers associated with agricultural burning. Machine learning models—Ridge Regression, XGBoost, and LSTM (Long Short-Term Memory) neural networks—were evaluated under high spatial and temporal variability (winter) and low variability (summer) conditions. Ridge Regression showed the best performance, achieving the highest $R^2$ (0.97 in winter, 0.93 in summer) and the lowest mean squared errors. XGBoost showed strong predictive capabilities but tended to overestimate moderate pollution events, while LSTM systematically underestimated PM2.5 levels in December. The residual analysis confirmed that Ridge Regression provided the most stable predictions, capturing extreme pollution episodes effectively, whereas XGBoost exhibited larger outliers. The study proved the potential of low-cost sensor networks and machine learning in urban air quality forecasting focused on rare smog episodes (RSEs). Full article

The hydrological response of a watershed is strongly influenced by the spatiotemporal dynamics of rainfall. Rainfall events of similar magnitude can produce markedly different flood processes due to variations in the spatiotemporal patterns of rainfall, posing significant challenges for flood forecasting under complex rainfall scenarios. Traditional methods typically rely on high-resolution or synthetic rainfall data to characterize the scale, direction and velocity of rainstorms, in order to analyze their impact on the flood process. These studies have shown that storms traveling along the main river channel tend to exert the greatest impact on flood processes. Therefore, tracking the movement of the rainfall center along the flow direction, especially when only rain gauge data are available, can reduce model complexity while maintaining forecast accuracy and improving model applicability. This study proposes a machine learning-based time-variable instantaneous unit hydrograph that integrates rainfall spatiotemporal dynamics using quantitative spatial indicators. To overcome limitations of traditional variable unit hydrograph methods, a pre-training and fine-tuning strategy is employed to link the unit hydrograph S-curve with rainfall spatial distribution. First, synthetic pre-training data were used to enable the machine learning model to learn the shape of the S-curve and its general pattern of variation with rainfall spatial distribution. Then, real flood data were employed to learn the actual runoff routing characteristics of the study area. The improved model allows the unit hydrograph to adapt dynamically to rainfall evolution during the flood event, effectively capturing hydrological responses under varying spatiotemporal patterns. The case study shows that the improved model exhibits superior performance across all runoff routing metrics under spatiotemporal rainfall variability. The improved model increased the simulation qualified rate for historical flood events, with significant rainfall center movement during the event from 63% to 90%. This study deepens the understanding of how rainfall dynamics influence watershed response and enhances hourly-scale flood forecasting, providing support for disaster early warning with strong theoretical and practical significance. Full article

Wildfires increasingly threaten human life, ecosystems, and infrastructure, with events like the 2025 Palisades and Eaton fires in Los Angeles County underscoring the urgent need for more advanced prediction frameworks. Existing physics-based and deep-learning models struggle to capture dynamic wildfire spread across both 2D and 3D domains, especially when incorporating real-time, multimodal geospatial data. This paper explores how generative artificial intelligence (AI) models—such as GANs, VAEs, and transformers—can serve as transformative tools for wildfire prediction and simulation. These models offer superior capabilities in managing uncertainty, integrating multimodal inputs, and generating realistic, scalable wildfire scenarios. We adopt a new paradigm that leverages large language models (LLMs) for literature synthesis, classification, and knowledge extraction, conducting a systematic review of recent studies applying generative AI to fire prediction and monitoring. We highlight how generative approaches uniquely address challenges faced by traditional simulation and deep-learning methods. Finally, we outline five key future directions for generative AI in wildfire management, including unified multimodal modeling of 2D and 3D dynamics, agentic AI systems and chatbots for decision intelligence, and real-time scenario generation on mobile devices, along with a discussion of critical challenges. Our findings advocate for a paradigm shift toward multimodal generative frameworks to support proactive, data-informed wildfire response. Full article

This study extends our prior blockchain-based traceability framework, WEave, for application to a furniture supply chain scenario, while using the original multi-tier apparel supply chain as an anchoring use case. We integrate circular economy principles such as product reuse, recycling traceability, and full lifecycle transparency to bolster sustainability and resilience in supply chains by enabling data-driven accountability and tracking for closed-loop resource flows. The enhanced approach can track post-consumer returns, use of recycled materials, and second-life goods, all represented using a closed-loop supply chain topology. We describe the extended network architecture and smart contract logic needed to capture circular lifecycle events, while proposing new metrics for evaluating lifecycle traceability and reuse auditability. To validate the extended framework, we outline simulation experiments that incorporate circular flows and cross-industry scenarios. Results from these simulations indicate improved transparency on recycled content, audit trails for returned products, and acceptable performance overhead when scaling to different product domains. Finally, we offer conclusions and recommendations for implementing WEave functionality into real-world settings consistent with the goals of digital, resilient, and sustainable supply chains. Full article

Membrane fouling remains a major challenge in full-scale membrane bioreactor (MBR) systems, reducing operational efficiency and increasing maintenance needs. This study introduces a predictive and analytic framework for membrane fouling by integrating artificial intelligence (AI)-driven feature engineering and explainable AI (XAI) using real-world data from an MBR treating food processing wastewater. The framework refines the target parameter to specific flux (flux/transmembrane pressure (TMP)), incorporates chemical oxygen demand (COD) removal efficiency to reflect biological performance, and applies a moving average function to capture temporal fouling dynamics. Among tested models, CatBoost achieved the highest predictive accuracy (R² = 0.8374), outperforming traditional statistical and other machine learning models. XAI analysis identified the food-to-microorganism (F/M) ratio and mixed liquor suspended solids (MLSSs) as the most influential variables affecting fouling. This robust and interpretable approach enables proactive fouling prediction and supports informed decision making in practical MBR operations, even with limited data. The methodology establishes a foundation for future integration with real-time monitoring and adaptive control, contributing to more sustainable and efficient membrane-based wastewater treatment operations. However, this study is based on data from a single full-scale MBR treating food processing wastewater and lacks severe fouling or cleaning events, so further validation with diverse datasets is needed to confirm broader applicability. Full article

In North Carolina (NC), roughly 1 in 4 residents rely on private wells for drinking water. Given the potential for flooding to impact well water quality, which poses serious health hazards to well users, accurate near real-time precipitation estimates are vital for guiding outreach and mitigation efforts. GPM IMERG precipitation data provides a solution for this need. Previous studies have shown that IMERG version 06 performs well throughout NC for capturing event totals. This study investigates changes in precipitation performance from IMERG version 06 to version 07 in NC and surrounding regions. There was significant improvement pertaining to errors quantifying the magnitude of precipitation events; the mean error in event precipitation decreased 75–85%, bias decreased 65–80%, and the root mean square error decreased 15–30% for Early, Late, and Final products as compared to event totals from in situ precipitation gauges. V07 shows improved performance during events in colder conditions, in mountainous regions, and with higher, prolonged intensities. During Hurricane Florence (September 2018), v07 improved precipitation estimates in regions with higher rainfall totals. These findings demonstrate the potential of the IMERG v07 Early and Late data products for the creation of accurate and timely flood models in emergency response applications. Full article

By integrating EarthCARE W-band doppler cloud radar observations with GPM Ku/Ka-band dual-frequency precipitation radar data, this study constructs a novel global “pseudo tripe-frequency” radar coincidence dataset comprising 2886 coincidence events (about one-third of the events detected precipitation), aiming to systematically investigating band-dependent responses to cloud and precipitation structure. Results demonstrate that the W-band is highly sensitive to high-altitude cloud particles and snowfall (reflectivity < 0 dBZ), yet it experiences substantial signal attenuation under heavy precipitation conditions, and with low-altitude reflectivity reductions exceeding 50 dBZ, its probability density distribution is more widespread, with low-altitude peaks increasing first, and then decreasing as precipitation increases. In contrast, the Ku and Ka-band radars maintain relatively stable detection capabilities, with attenuation differences generally within 15 dBZ, but its probability density distribution exhibits multiple peaks. As the precipitation rate increases, the peak value of the dual-frequency ratio (Ka/W) gradually rises from approximately 10 dBZ to 20 dBZ, and can even reach up to 60 dBZ under heavy rainfall conditions. Several cases analyses reveal clear contrasts: In stratiform precipitation regions, W-band radar reflectivity is higher above the melting layer than below, whereas the opposite pattern is observed in the Ku and Ka bands. Doppler velocities exceeding 5 m s⁻¹ and precipitation rates surpassing 30 mm h⁻¹ exhibit strong positive correlations in convection-dominated regimes. Furthermore, the dataset confirms the impact of ice–water cloud phase interactions and terrain-induced precipitation variability, underscoring the complementary strengths of multi-frequency radar observations for capturing diverse precipitation processes. Full article

Phosphorus (P) is a problematic waterborne pollutant, and considerable efforts have been taken to monitor its presence and transport in locales struggling with eutrophication. Most historical P datasets consist of intermittent grab samples, necessitating the construction of surrogacy models to explore P at high resolutions. In Iowa, models using historical data to relate turbidity to particulate P (PartP) have successfully been created. However, it is unknown how comprehensively historical datasets reflect Iowa’s hydrologic conditions and how well these models perform during flows not well represented within the existing data. In this study, we analyzed historical P datasets from 16 major Iowa rivers to determine how well they captured the rivers’ full range of streamflow conditions. While these datasets contained sufficient samples during low and average flows, they typically under-sampled high flows—containing few values above the 85–95th percentiles. Therefore, we collected new data in each river during wet conditions, with ~300 samples taken from 2021 to 2024. These new sampling results largely aligned with the existing surrogacy models and slightly improved model performance, suggesting that utilizing turbidity to predict PartP is appropriate in nearly all streamflow conditions. These findings may prove consequential for robustly modeling PartP due to its dynamic nature and disproportionately high transport during wet weather events. Full article

In the landslide chain from pre-disaster conditions to landslide mitigation and recovery, time is an important factor in understanding the geological hazards process and managing landsides. Static knowledge graphs are unable to capture the temporal dynamics of landslide events. To address this limitation, we propose a systematic framework for constructing a multi-temporal knowledge graph of landslides that integrates multi-source temporal data, enabling the dynamic tracking of landslide processes. Our approach comprises three key steps. First, we summarize domain knowledge and develop a temporal ontology model based on the disaster chain management system. Second, we map heterogeneous datasets (both tabular and textual data) into triples/quadruples and represent them based on the RDF (Resource Description Framework) and quadruple approaches. Finally, we validate the utility of multi-temporal knowledge graphs through multidimensional queries and develop a web interface that allows users to input landslide names to retrieve location and time-axis information. A case study of the Zhangjiawan landslide in the Three Gorges Reservoir Area demonstrates the multi-temporal knowledge graph’s capability to track temporal updates effectively. The query results show that multi-temporal knowledge graphs effectively support multi-temporal queries. This study advances landslide research by combining static knowledge representation with the dynamic evolution of landslides, laying the foundation for hazard forecasting and intelligent early-warning systems. Full article

Dense video captioning involves identifying, localizing, and describing multiple events within a video. Capturing temporal and contextual dependencies between events is essential for generating coherent and accurate captions. To effectively capture temporal and contextual dependencies between events, we propose Dense Video Captioning with Dual Contextual, Systematic, and Linguistic Modules (DVC-DCSL), a novel dense video captioning model that integrates contextual, semantic, and linguistic modules. The proposed approach employs two uni-directional LSTMs (forward and backward) to generate distinct captions for each event. A caption selection mechanism then processes these outputs to determine the final caption. In addition, contextual alignment is improved by incorporating visual and textual features from previous video segments into the captioning module, ensuring smoother narrative transitions. Comprehensive experiments conducted using the ActivityNet dataset demonstrate that DVC-DCSL increases the Meteor score from 11.28 to 12.71, representing a 12% improvement over state-of-the-art models in the field of dense video captioning. These results highlight the effectiveness of the proposed approach in improving dense video captioning quality through contextual and linguistic integration. Full article