Search Results (1,216)

The accurate extraction of water bodies from remote sensing imagery is crucial for water resource monitoring and flood disaster warning. However, this task faces significant challenges due to complex land cover, large variations in water body morphology and spatial scales, and spectral similarities between water and non-water features, leading to misclassification and low accuracy. While deep learning-based methods have become a research hotspot, traditional convolutional neural networks (CNNs) struggle to represent multi-scale features and capture global water body information effectively. To enhance water feature recognition and precisely delineate water boundaries, we propose the AMU-Net model. Initially, an improved residual connection module was embedded into the U-Net backbone to enhance complex feature learning. Subsequently, a multi-scale attention mechanism was introduced, combining grouped channel attention with multi-scale convolutional strategies for lightweight yet precise segmentation. Thereafter, a dual-attention gated modulation module dynamically fusing channel and spatial attention was employed to strengthen boundary localization. Furthermore, a cross-layer geometric attention fusion module, incorporating grouped projection convolution and a triple-level geometric attention mechanism, optimizes segmentation accuracy and boundary quality. Finally, a triple-constraint loss framework synergistically optimized global classification, regional overlap, and background specificity to boost segmentation performance. Evaluated on the GID and WHDLD datasets, AMU-Net achieved remarkable IoU scores of 93.6% and 95.02%, respectively, providing an effective new solution for remote sensing water body extraction. Full article

Climate change is elevating temperatures, shifting weather patterns, and increasing frequency and severity of extreme weather events. Despite the urgency with which solutions are needed, relatively few studies comprehensively investigate the effectiveness of alternative flood risk management options under different climate conditions. Specifically, we are interested in a comparison of the effectiveness of resistance, nature-based, and managed retreat strategies. Using an integrated 1D-2D PCSWMM model, this paper presents a comprehensive investigation into the effectiveness of alternative adaptation strategies in reducing flood risks in Eastwick, a community of Philadelphia, PA, subject to fluvial, pluvial, and coastal flood hazards. While addressing the urgent public need to develop local solutions to this community’s flood problems, the research also presents transferable insights into the limitations and opportunities of different flood risk reduction strategies, manifested here by a levee, watershed-scale green stormwater infrastructure (GSI) program, and a land swap. The effectiveness of these options is compared, respectively, under compound climate change conditions, with the spatiotemporal patterns of precipitation and Delaware river tidal conditions based on Tropical Storm Isaias (2020). The hypothesis was that the GSI and managed retreat approaches would be superior to the levee, due to their intrinsic ability to address the compound climate hazards faced by this community. Indeed, the findings illustrate significant differences in the predicted flood extents, depths, and duration of flooding of the various options under both current and future climate scenarios. However, the ideal remedy to flooding in Eastwick is more likely to require an integrated approach, based on more work to evaluate cost-effectiveness, stakeholder preferences, and various logistical factors. The paper concludes with a call for integrating multiple strategies into multifunctional flood risk management. Full article

Incorporating frequency constraints into the optimum design of large-scale truss dome structures is crucial for maintaining seismic resilience, as the natural frequencies must remain within specified ranges. In this work, seven metaheuristic algorithms—including three variants of the Fitness–Distance–Balance-based Adaptive Guided Differential Evolution (FDB-AGDE), the Cheetah Optimizer (CO), the Bonobo Optimizer (BO), the Flood Algorithm (FLA), and the Lung Performance Optimization (LPO) are applied to solve high-dimensional truss sizing problems under strict frequency limitations. Their convergence characteristics and solution quality are systematically compared across multiple dome configurations. Besides traditional measures of computational efficiency and final weight minimization, a suite of statistical analyses is conducted: the Wilcoxon rank-sum test to assess pairwise performance significance, the Friedman test to establish overall rank ordering, and Cohen’s test to quantify effect sizes. The results reveal that LPO, BO, CO, and the first variant of FDB-AGDE consistently produce lighter feasible designs with lower variability, whereas FLA and other variants of FDB-AGDE exhibit heavier structures or higher dispersion. The findings underscore the value of robust, well-tuned metaheuristics and rigorous statistical evaluation in structural optimization, offering clear guidance for seismic-focused designers seeking both lightweight solutions and reliable performance across repeated runs. Full article

To address the challenges of complex task matching, limited semantic representation, and low recommendation efficiency in remote sensing data acquisition for natural disasters, this study proposes a semantic path-guided recommendation method based on a knowledge graph framework. A disaster-oriented remote sensing knowledge graph is constructed by integrating entities such as disaster types, remote sensing tasks, observation requirements, sensors, and satellite platforms. High-order meta-paths with semantic closure are designed to model task–resource relationships structurally. A Meta-Path2Vec embedding mechanism is employed to learn vector representations of nodes through path-constrained random walks and Skip-Gram training, capturing implicit semantic correlations between tasks and sensors. Cosine similarity and a Top-K ranking strategy are then applied to perform intelligent task-driven sensor recommendation. Experiments on multiple disaster scenarios—such as floods, landslides, and wildfires—demonstrate the model’s high accuracy and robust stability. An interactive recommendation system is also developed, integrating data querying, model inference, and visual feedback, validating the method’s practicality and effectiveness in real-world applications. This work provides a theoretical foundation and practical solution for intelligent remote sensing data matching in disaster contexts. Full article

Urban flooding presents increasingly complex challenges exacerbated by climate change, rapid urbanization, and aging infrastructure. This investigation combines planning theories and socio-hydrological modelling to create a planning-adaptable urban flood management strategy. The case study of Chiba Prefecture, Japan, demonstrates this approach in depth. By applying the Social-Ecological-Technological Systems (SETS) framework in combination with planning theories, the study has identified the relationship between the conventional engineered methods and the newly introduced environmentally friendly (nature-based) solutions. Our findings, which are based on content analysis of 23 official statutory planning documents, indicate that there is a significant focus on the conservation of ecosystems and green infrastructure balanced with issues of emergency planning and community engagement. One of the points that the results highlight is integrating the ecological, social and technological aspects in order to create flood management policies that are both robust and fair. This integrated approach offers a robust framework for mitigating flood risks while promoting sustainable urban development and long-term community resilience. Full article

This study applies the Integrated Modification Methodology (IMM) to assess how morphology-driven, nature-based solutions reduce urban heat island (UHI) effects and flooding in Rio de Janeiro’s Cidade Nova. Multi-scale GIS diagnostics identify green continuity and vertical permeability as critical weaknesses. Simulations (Ladybug/Dragonfly) and hydrological modelling (rational method) quantify the intervention’s impact, including greening, material retrofits, and drainage upgrades. Results show a 38% increase in albedo, a 13% reduction in volumetric heat capacity, and a 30% drop in thermal conductivity. These changes reduce the peak UHI by 0.2 °C hourly, narrowing the urban–rural temperature gap to 3.5 °C (summer) and 4.3 °C (winter). Hydrologically, impervious cover decreases from 22% to 15%, permeable surfaces rise from 9% to 29%, and peak runoff volume drops by 27% (16,062 to 11,753 m³/h), mitigating flood risks. Green space expands from 7.8% to 21%, improving connectivity by 50% and improving park access. These findings demonstrate that IMM-guided interventions effectively enhance thermal and hydrological resilience in dense tropical cities, aligning with climate adaptation and the Sustainable Development Goals. Full article

Pan-connected interlayers are widely present in oil reservoirs, forming flow channels at different positions. However, conventional profile control agents struggle to plug deep interlayer channels in reservoirs, limiting the swept volume of injected water. Additionally, a clear methodology for physically simulating pan-connected reservoirs with interlayer channels and calculating interchannel flow rates remains lacking. In this study, a physical model of pan-connected interlayer reservoirs was constructed to carry out deformable gel particles (DGPs) plugging experiments on interlayer channels. A mass conservation-based flow rate calculation method for interlayer channels with iterative solution was proposed, revealing the variation law of interlayer channel flow rates during DGP injection and subsequent water flooding. Finally, oil displacement and DGP profile control experiments in pan-connected interlayer reservoirs were conducted. The study shows that during DGP injection, injected water enters the potential layer through interlayer channels in the middle and front of the water-channeling layer and bypasses back to the water-channeling layer through channels near the production well. With the increase in DGP injection volume, the flow rate of each channel increases. During subsequent water flooding, DGP breakage leads to a rapid decline in its along-path plugging capability, so water bypasses back to the water-channeling layer from the potential layer through all interlayer channels. As the DGP injection volume increases, the flow rate of each channel decreases. Large-volume DGPs can regulate interlayer channeling reservoirs in the high water cut stage. Its effectiveness mechanism involves particle migration increasing the interlayer pressure difference, which drives injected water to sweep from the water-channeling layer to the potential layer through interlayer channels, improving oil recovery by 19.74%. The flow characteristics of interlayer channels during DGP injection play a positive role in oil displacement, so the oil recovery degree in this process is greater than that in the subsequent water flooding stage under each injection volume condition. The core objective of this study is to investigate the plugging mechanism of DGPs in pan-connected interlayer channels of high-water-cut reservoirs, establish a method to quantify interlayer flow rates, and reveal how DGPs regulate flow redistribution to enhance oil recovery. Full article

Floods are among the most devastating natural disasters; predicting their depth and extent remains a global challenge. Machine Learning (ML) models have demonstrated improved accuracy over traditional probabilistic flood mapping approaches. While previous studies have developed ML-based models for specific local regions, this study aims to establish a methodology for estimating flood depth on a global scale using ML algorithms and freely available datasets—a challenging yet critical task. To support model generalization, 45 catchments from diverse geographic regions were selected based on elevation, land use, land cover, and soil type variations. The datasets were meticulously preprocessed, ensuring normality, eliminating outliers, and scaling. These preprocessed data were then split into subgroups: 75% for training and 25% for testing, with six additional unseen catchments from the USA reserved for validation. A sensitivity analysis was performed across several ML models (ANN, CNN, RNN, LSTM, Random Forest, XGBoost), leading to the selection of the Random Forest (RF) algorithm for both flood inundation classification and flood depth regression models. Three regression models were assessed for flood depth prediction. The pixel-based regression model achieved an R² of 91% for training and 69% for testing. Introducing a pixel clustering regression model improved the testing R² to 75%, with an overall validation (for unseen catchments) R² of 64%. The catchment-based clustering regression model yielded the most robust performance, with an R² of 83% for testing and 82% for validation. The developed ML model demonstrates breakthrough computational efficiency, generating complete flood depth predictions in just 6 min—a 225× speed improvement (90–95% time reduction) over conventional HEC-RAS 6.3 simulations. This rapid processing enables the practical implementation of flood early warning systems. Despite the dramatic speed gains, the solution maintains high predictive accuracy, evidenced by statistically robust 95% confidence intervals and strong spatial agreement with HEC-RAS benchmark maps. These findings highlight the critical role of the spatial variability of dependencies in enhancing model accuracy, representing a meaningful approach forward in scalable modeling frameworks with potential for global generalization of flood depth. Full article

This paper is aimed at landscape managers and designers. It looks at 123 real-world coastal landscape projects and organizes them into clear design categories, i.e., wetland restoration, hybrid infrastructure, or urban green spaces. We looked at how these projects were framed (whether they focused on climate adaptation, flood protection, or other goals) and how they tracked performance. We are hoping to bring some clarity to a very scattered field, helping us to see patterns in what is actually being carried out in terms of landscape interventions and increasing sea levels. We are hoping to provide a practical reference for making better, more climate-responsive design decisions. Coastal cities face escalating climate-driven threats from increasing sea levels and storm surges to urban heat islands. These threats are driving increased interest in nature-based solutions (NbSs) as green adaptive alternatives to traditional gray infrastructure. Despite an abundance of individual case studies, there have been few systematic syntheses aimed at landscape designers and managers linking design typologies, project framing, and performance outcomes. This study addresses this gap through a meta-synthesis of 123 implemented coastal landscape interventions aimed directly at landscape-oriented research and professions. Flood risk reduction was the dominant framing strategy (30.9%), followed by climate resilience (24.4%). Critical evidence gaps emerged—only 1.6% employed integrated monitoring approaches, 30.1% provided ambiguous performance documentation, and mean monitoring quality scored 0.89 out of 5.0. While 95.9% of the projects acknowledged SLR as a driver, only 4.1% explicitly integrated climate projections into design parameters. Community monitoring approaches demonstrated significantly higher ecosystem service integration, particularly cultural services (36.4% vs. 6.9%, $p<0.001$), and enhanced monitoring quality (mean score 1.64 vs. 0.76, $p<0.001$). Implementation barriers spanned technical constraints, institutional fragmentation, and data limitations, each affecting 20.3% of projects. Geographic analysis revealed evidence generation inequities, with systematic underrepresentation of high-risk regions (Africa: 4.1%; Latin America: 2.4%) versus concentration in well-resourced areas (North America: 27.6%; Europe: 17.1%). Full article

Polymer flooding is an enhanced oil recovery (EOR) technique that improves oil extraction by injecting polymer solutions into reservoirs. However, the disposal and treatment of polymer flooding waste liquids (PFWL) present significant challenges due to their high viscosity, complex molecular structure, and environmental impact. This study investigates the shear-induced degradation of polymer solutions, focusing on rheological properties, particle size distribution, and morphological changes under controlled shear conditions. Experimental results show that shear forces significantly reduce the viscosity of polymer solutions, with shear rates of 4285.36 s⁻¹ in the rotating domain and 3505.21 s⁻¹ in the fixed domain. The particle size analysis reveals a significant reduction in average particle size, indicating polymer aggregate breakup. SEM images confirm these morphological changes. Additionally, numerical simulations using a power-law model highlight the correlation between shear rate, wall shear stress, and polymer degradation efficiency. This study suggests that optimizing rotor–stator configurations with high shear forces is essential for efficient polymer degradation, offering insights for designing more effective polymer waste liquid treatment systems in oilfields. Full article

Urban flooding and the management of stormwater present significant challenges that necessitate innovative and sustainable solutions. This research examines the effectiveness of green stormwater infrastructure (GSI) for resilient storm sewer systems using the Storm Water Management Model (SWMM), based on customized local climate scenarios. Daily climate data downscaled by four CMIP6 models—CESM2, GFDL-CM4, GFDL-ESM4, and NorESM2-MM—was used. The daily data was disaggregated into 15 min temporal resolution using the HyetosMinute R-package. Two GSI types—bio-retention and rain gardens—were evaluated with a maximum coverage of 30%. The analysis focuses on two future climate scenarios, SSP2-4.5 and SSP5-8.5, predicted under the Shared Socioeconomic Pathways (SSPs) framework. The performance of the stormwater network was assessed for mid-century (2041–2060) and late century (2081–2100), both before and after integration of GSI. Three performance metrics were applied: node flooding volume, number of nodes flooded, and pipe surcharging duration. The simulation results showed an average reduction in flooding volumes ranging between 86 and 98% over the area after integration of GSI. Similarly, reductions ranging between 78 and 89% and between 75 and 90% were observed in pipe surcharging duration and number of nodes vulnerable to flooding, respectively, following GSI. These findings underscore the potential of GSI in fostering sustainable urban water management and enhancement of sustainable development goals (SDGs). Full article

This study examined a novel ethoxy-segment-regulated hydrophobic associative amphiphilic copolymer, P(AA-AAEO_n), and systematically evaluated its solution self-assembly behavior and enhanced oil recovery (EOR) performance. The influence of ethylene oxide (EO) chain length and polymer concentration on particle size distribution and aggregation morphology was analyzed using dynamic light scattering (DLS). The results revealed a concentration-dependent transition from intramolecular to intermolecular association, accompanied by a characteristic decrease followed by an increase in hydrodynamic diameter. At a fixed AA:AAEO_n molar ratio (400:1), increasing EO segment length increased aggregate size and improved colloidal stability. Viscometric analysis showed that longer EO chains markedly increased molecular chain flexibility and solution viscosity. Interfacial tension measurements demonstrated superior interfacial activity of P(AA-AAEO_n) compared to polyacrylic acid (PAA), and longer EO chains further reduced oil–water interfacial tension. Emulsification tests verified its strong ability to emulsify crude oil. Sandpack flooding experiments and micromodel studies demonstrated effective conformance control and high displacement efficiency, achieving up to 30.65% incremental oil recovery. These findings offered essential insights for designing hydrophobic associative polymers with tunable interfacial properties for EOR applications. Full article

Small and medium-sized enterprises (SMEs) are increasingly targeted by cyber threats but often lack the financial and technical resources to implement advanced security systems. This paper presents HoneyLite, a lightweight and dynamic honeypot-based security solution specifically designed to meet the constraints and cybersecurity needs of SMEs. Unlike traditional honeypots, HoneyLite integrates real-time network traffic analysis with automated malware detection via the VirusTotal API, enabling it to identify a wide range of cyber threats, including TCP scans, FTP/SSH intrusions, ICMP flood attacks, and malicious file uploads. Developed using open-source tools, the system operates with minimal resource overhead and is validated within a simulated virtual environment. It also generates detailed threat reports to support incident analysis and response. By combining affordability, adaptability, and comprehensive threat visibility, HoneyLite offers a practical and scalable solution to help SMEs detect, analyze, and respond to modern cyberattacks in real time. Full article

To address the issues of poor thermal stability, inadequate salt tolerance, and environmental risks in conventional gel systems for the development of high-temperature, high-salinity heterogeneous reservoirs, a triple-synergy gel system comprising anionic polyacrylamide (APAM), polyethyleneimine (PEI), and phenolic resin (SMP) was developed in this study. The optimal synthesis parameters—APAM of 180 mg/L, PEI:SMP = 3:1, salinity of 150,000 ppm, and temperature of 110 °C—were determined via response surface methodology, and a time–viscosity model was established. Compared with existing binary systems, the proposed gel exhibited a mass retention rate of 93.48% at 110 °C, a uniform porous structure (pore size of 2–8 μm), and structural stability under high salinity (150,000 ppm). Nuclear magnetic resonance displacement tests showed that the utilization efficiency of crude oil in 0.1–1 μm micropores increased to 21.32%. Parallel dual-core flooding experiments further confirmed the selective plugging capability in heterogeneous systems with a permeability contrast of 10:1: The high-permeability layer (500 mD) achieved a plugging rate of 98.7%, while the recovery factor of the low-permeability layer increased by 13.6%. This gel system provides a green and efficient profile control solution for deep, high-temperature, high-salinity reservoirs. Full article

The offshore reservoir development involves large injection and production rates and high injection pressures. High-permeability flow channels usually occur in offshore unconsolidated heavy-oil reservoirs during long-term water flux, substantially impacting the production performance. As one important method for identifying channeling, the numerical simulation method with a full-fidelity model is hampered by the low computational efficiency of the history matching process. The GPSNet model is extended for polymer flooding simulations, incorporating complex mechanisms including adsorption and shear-thinning effects, with solutions obtained through a fully implicit numerical scheme. Four flow channel characteristic parameters are proposed, and an evaluation factor M for flow channel identification is established with the comprehensive evaluation method. Finally, the field application of the GPSNet model is made and validated by the tracer interpretation result. The history matching speed based on the GPSNet model is 58 times faster than the full-fidelity ECLIPSE model. In addition, the application demonstrates a high degree of consistency with tracer monitoring results, confirming the accuracy and field feasibility. The new method enables rapid and accurate identification and prediction of large and dominant channels, offering effective guidance for targeted treatment of channels and sustainable development of polymer flooding. Full article