Search Results (1,297)

Mountain protected areas in the tropical Andes experience localized yet severe soil erosion that threatens erosion-regulating services and downstream water–energy security. We mapped soil loss at 30 m using the Revised Universal Soil Loss Equation (RUSLE) and quantified the erosion-control service in El Cajas National Park, Ecuador (28,544 ha) using an NDVI-based index. Replacing categorical land cover C factors with a continuous NDVI surface increased the park-wide soil loss estimate by ∼58%, yielding an area-weighted mean of 5.3 t ha⁻¹ yr⁻¹ and local maxima of 120 t ha⁻¹ yr⁻¹ on steep and sparsely vegetated escarpments. Relative to a bare soil scenario, existing páramo grasslands, shrub mosaics, and scattered Polylepis woodlots avert 95% of potential erosion, quantifying the service supplied by vegetation. Between 2023 and 2024, a ∼60% rise in mean NDVI more than doubled the area delivering moderate-to-high erosion control. A hot-spot analysis further identified ∼30 km² (≈5% of the park) where high modeled soil loss coincides with low protection; these clusters generate ∼80% of predicted sediment and constitute priority targets for restoration or visitor use regulation. The integrated RUSLE–NDVI–EC approach provides a concise and transferable screening tool for aligning conservation investments with Ecuador’s restoration pledges and for safeguarding critical hydrological services in Andean protected areas. Full article

Urban water supply networks are prone to complex noise interference, which significantly degrades the performance of data-driven forecasting models. Conventional denoising techniques, such as standard Variational Mode Decomposition (VMD), often rely on empirical parameter selection or optimize only a subset of parameters, lacking a robust mechanism for identifying noise-dominant components post-decomposition. To address these issues, this paper proposed a novel denoising framework termed Dynamic Hyperbolic Tangent PSO-optimized VMD (DHTPSO-VMD). The DHTPSO algorithm adaptively adjusts inertia weights and cognitive/social learning factors during iteration, mitigating the local optima convergence typical of traditional PSO and enabling automated VMD parameter selection. Furthermore, a dual-criteria screening strategy based on Variance Contribution Rate (VCR) and Correlation Coefficient Metric (CCM) is employed to accurately identify and eliminate noise-related Intrinsic Mode Functions (IMFs). Validation using pressure data from District A in Zhejiang Province, China, demonstrated that the proposed DHTPSO-VMD method significantly outperforms benchmark approaches (PSO-VMD, EMD, SABO-VMD, GWO-VMD) in terms of Signal-to-Noise Ratio (SNR), Mean Absolute Error (MAE), and Mean Square Error (MSE). Subsequent forecasting experiments using an Informer model showed that signals preprocessed with DHTPSO-VMD achieved superior prediction accuracy (R² = 0.948924), underscoring its practical utility for smart water supply management. Full article

Building clustering is a key challenge in cartographic generalization, where the goal is to group spatially related buildings into semantically coherent clusters while preserving the true distribution patterns of urban structures. Existing methods often rely on either spatial distance or building feature similarity alone, leading to clusters that sacrifice either accuracy or spatial continuity. Moreover, most deep learning-based approaches, including graph attention networks (GATs), fail to explicitly incorporate spatial distance constraints and typically restrict message passing to first-order neighborhoods, limiting their ability to capture long-range structural dependencies. To address these issues, this paper proposes LA-GATs, a multi-feature constrained and spatially adaptive building clustering network. First, a Delaunay triangulation is constructed based on nearest-neighbor distances to represent spatial topology, and a heterogeneous feature matrix is built by integrating architectural spatial features, including compactness, orientation, color, and height. Then, a spatial distance-constrained attention mechanism is designed, where attention weights are adjusted using a distance decay function to enhance local spatial correlation. A second-order neighborhood aggregation strategy is further introduced to extend message propagation and mitigate the impact of triangulation errors. Finally, spectral clustering is performed on the learned similarity matrix. Comprehensive experimental validation on real-world datasets from Xi’an and Beijing, showing that LA-GATs outperforms existing clustering methods in both compactness, silhouette coefficient and adjusted rand index, with up to about 21% improvement in residential clustering accuracy. Full article

Vulnerability to hazards is a critical global issue, as it not only depends on the magnitude of natural hazards but also on the underlying social and economic conditions of communities. Understanding these factors is essential for designing effective risk reduction strategies and informed policy decisions. The objective of this research is to define a social vulnerability index (SoVI) and to analyse its distribution at the provincial and urban levels by applying different aggregation methods. This study provides a novel approach by examining the sensitivity of the index to different weighting methodologies, addressing a gap in the literature regarding the robustness of social vulnerability measures. An alternative approach is provided to determine the sensitivity of the SoVI in regions, in addition to understanding the dynamics of the socioeconomic characteristics considered in the territory and contributing to the theoretical and normative discussion of the construction of the index. To meet the objective, a sensitivity analysis is provided through different methods of weighting the vulnerability dimensions. The results indicate that the distribution of the SoVI in the provinces of Ecuador is heterogeneous, highlighting the importance of considering local socioeconomic contexts in vulnerability assessments. Additionally, the study shows that the values of the constructed index are sensitive to the weighting methods of the dimensions, which underscores the need for a careful selection of aggregation techniques to ensure reliable policy implications. It was also possible to identify that when social vulnerability is analysed at the city level, these show higher values than the corresponding provinces, challenging the common assumption that urban areas inherently provide better living conditions. This finding contributes to the ongoing debate on the impacts of rapid urbanization on social vulnerability. Full article

Semantic segmentation of high-spatial-resolution (HSR) aerial imagery is critical for applications such as urban planning and environmental monitoring, yet challenges, including scale variation, intra-class diversity, and inter-class confusion, persist. This study proposes a deep learning framework that integrates convolutional networks (CNNs) with context-enhanced superpixel generation, using ConvNeXt as the backbone for feature extraction. The framework incorporates two key modules, namely, a deep superpixel module (Spixel) and a global context modeling module (GC-module), which synergistically generate context-weighted superpixel embeddings to enhance scene–object relationship modeling, refining local details while maintaining global semantic consistency. The introduced approach achieves mIoU scores of 84.54%, 90.59%, and 64.46% on diverse HSR aerial imagery benchmark datasets (Vaihingen, Potsdam, and UV6K), respectively. Ablation experiments were conducted to further validate the contributions of the global context modeling module and deep superpixel modules, highlighting their synergy in improving segmentation results. This work facilitates precise spatial detail preservation and semantic consistency in HSR aerial imagery interpretation tasks, particularly for small objects and complex land cover classes. Full article

This paper evaluates botanical gardens in terms of their potential for tourist valorization, aiming to identify the garden with the highest tourist appeal and integration opportunities within the tourist market. Based on a literature review and established attractiveness criteria, a methodological framework using multi-criteria decision-making was developed to compare and rank the botanical gardens. The empirical part of the study focuses on botanical gardens in Split–Dalmatia County, including six gardens evaluated across nine criteria. Eight local tourism experts assessed the importance of these criteria and the gardens’ performance. The fuzzy SiWeC (SImple WEight Calculation) method was used to determine the importance of each criterion. The fuzzy TOPSIS method (Technique for Order Preference by Similarity to Ideal Solution) was used to measure the potential of botanical gardens. The main results obtained with this approach showed that the most important criteria are C4—Visitor content and C3—Biodiversity conservation. The Botanical Garden of Primary School Ostrog has the greatest potential, followed by the Botanical Garden Split. All observed botanical gardens have excellent tourist potential, with minimal differences in ranking among them. These findings demonstrate that botanical gardens play a key role in diversifying the tourist offer, reducing seasonality, and increasing the overall attractiveness of destinations. They also contribute to raising environmental awareness and emphasizing the importance of nature conservation and sustainable development, aligning with the increasing tourist interest in natural and ecologically responsible experiences. This study offers practical insights, as the results can assist garden management, tourism communities, and policymakers in developing and promoting strategies. Additionally, the framework proposed can be applied in other regional and international contexts. Full article

International building sustainability assessment tools (BSATs) offer a comprehensive framework for assessing environmental, economic, and social sustainability. However, these tools cannot fill the gap between their standards and the regional needs of developing countries such as Morocco. This paper presents a new framework to assess the sustainability of buildings in Morocco. The methodology proposed is the Fuzzy Delphi method to minimize the list of indicators with the help of 14 local experts and give an appropriate weight to the indicators and sub-indicators. The two-round analysis found a balanced weighting for the environmental, economic, and social dimensions, with the social pillar ranked highest in importance. A hierarchical framework of six consensus-based categories and 63 sub-indicators was developed. Consensus was measured using the dispersion threshold approach ≤ 0.2. The results show that waste and pollution (0.80), adaptability and resilience (0.78), and resources (0.75) are prioritized over the innovation category. Notably, sewage management, water reuse, and public infrastructure emerged as critical sub-indicators. A comparative evaluation against local BSATs from the region—Ethiopia, Sub-Saharan Africa, Saudi Arabia, and Oman—revealed convergence in core indicators like energy and water, yet divergence in economic and resilience criteria, reflecting regional specificities. This work contributes to the literature by presenting a validated, expert-driven assessment tool that aligns with local needs, offering a practical basis for national green certification and sustainable housing policy in Morocco and similar contexts. Full article

We propose State-DynAttn, a hybrid architecture for robust air traffic flow prediction under weather disruptions, which integrates state-space models (SSMs) with dynamic graph attention to address the challenges of long-range dependency modeling and adaptive spatial–temporal relationship learning. The increasing complexity of air traffic systems, exacerbated by unpredictable weather events, demands methods that can simultaneously capture global temporal patterns and localized disruptions; existing approaches often struggle to balance these requirements efficiently. The proposed method employs two parallel branches: an SSM branch for continuous-time recurrent modeling of long-range dependencies with linear complexity, and a dynamic graph attention branch that adaptively computes node-pair weights while incorporating weather severity features through sparsification strategies for scalability. These branches are fused via a data-dependent gating mechanism, enabling the model to dynamically prioritize either global temporal dynamics or localized spatial interactions based on input conditions. Moreover, the architecture leverages memory-efficient attention computation and HiPPO initialization to ensure stable training and inference. Experiments on real-world air traffic datasets demonstrate that State-DynAttn outperforms existing baselines in prediction accuracy and robustness, particularly under severe weather scenarios. The framework’s ability to handle both gradual traffic evolution and abrupt disruption-induced changes makes it suitable for real-world deployment in air traffic management systems. Furthermore, the design principles of State-DynAttn can be extended to other spatiotemporal prediction tasks where long-range dependencies and dynamic relational structures coexist. This work contributes a principled approach to hybridizing state-space models with graph-based attention, offering insights into the trade-offs between computational efficiency and modeling flexibility in complex dynamical systems. Full article

The unique hollowed-out catamaran hulls and complex environmental conditions in lake areas hinder traditional ranging algorithms (combining target detection and stereo matching) from accurately obtaining depth information near the center of ships. This not only impairs the navigation of electric tourist boats but also leads to high computing resource consumption. To address this issue, this study proposes a ranging method integrating improved ORB (Oriented FAST and Rotated BRIEF) with stereo vision technology. Combined with traditional optimization techniques, the proposed method calculates target distance and angle based on the triangulation principle, providing a rough alternative solution for the “gap period” of stereo matching-based ranging. The method proceeds as follows: first, it acquires ORB feature points with relatively uniform global distribution from preprocessed binocular images via a local feature weighting approach; second, it further refines feature points within the ROI (Region of Interest) using a quadtree structure; third, it enhances matching accuracy by integrating the FLANN (Fast Library for Approximate Nearest Neighbors) and PROSAC (Progressive Sample Consensus) algorithms; finally, it applies the screened matching point pairs to the triangulation method to obtain the position and distance of the target ship. Experimental results show that the proposed algorithm improves processing speed by 6.5% compared with the ORB-PROSAC algorithm. Under ideal conditions, the ranging errors at 10m and 20m are 2.25% and 5.56%, respectively. This method can partially compensate for the shortcomings of stereo matching in ranging under the specified lake area scenario. Full article

Non-destructive testing methods are essential in civil engineering applications, such as evaluating the compressive strength of masonry. This paper presents a fuzzy signature model based on non-destructive in situ measurements and visual inspection, applying weighted geometric mean aggregation in the signature vertices determined by experts. The weights of the aggregation terms were optimized using the Monte Carlo method, genetic algorithm and particle swarm algorithm to ensure that the evaluation by the signature aligned with the results of destructive tests performed on existing masonry. The results of the methods were compared for single and multiple assembled masonry structures using the same objective function. All three methods provided relatively high confidence in finding the extreme values of the objective function on a generated dataset, which accounted for the correlations observed in actual measurements. Accordingly, validation based on real data yielded the expected results, thus demonstrating the model’s suitability for practical application. This study assessed the inherent, analyzing whether symmetric or asymmetric weight distributions affected evaluation consistency. While symmetric weighting simplified aggregation, asymmetry allowed local structural irregularities to be highlighted. In addition, the cost analysis of the optimization methods revealed a disparity in computational cost increments between the two approaches. The presented work outlines the advantages of the different methods and their applicability to structural assessment. Full article

Open-weight generative large language models (LLMs) can be freely downloaded and modified. Yet, little empirical evidence exists on how these models are systematically altered and redistributed. This study provides a large-scale empirical analysis of safety-modified open-weight LLMs, drawing on 8608 model repositories and evaluating 20 representative modified models on unsafe prompts designed to elicit, for example, election disinformation, criminal instruction, and regulatory evasion. This study demonstrates that modified models exhibit substantially higher compliance: while an average of unmodified models complied with only 19.2% of unsafe requests, modified variants complied at an average rate of 80.0%. Modification effectiveness was independent of model size, with smaller, 14-billion-parameter variants sometimes matching or exceeding the compliance levels of 70B parameter versions. The ecosystem is highly concentrated yet structurally decentralized; for example, the top 5% of providers account for over 60% of downloads and the top 20 for nearly 86%. Moreover, more than half of the identified models use GGUF packaging, optimized for consumer hardware, and 4-bit quantization methods proliferate widely, though full-precision and lossless 16-bit models remain the most downloaded. These findings demonstrate how locally deployable, modified LLMs represent a paradigm shift for Internet safety governance, calling for new regulatory approaches suited to decentralized AI. Full article

The quality of groundwater, a crucial freshwater resource in cold regions, directly affects human health. This study used groundwater quality monitoring data collected in the central Songnen Plain in 2014 and 2022 as a case study. The improved DRASTICL model was used to assess the vulnerability index, while water quality indicators were selected using a random forest algorithm and combined with the entropy-weighted groundwater quality index (E-GQI) approach to realize water quality assessment. Furthermore, self-organizing maps (SOM) were used for pollutant source analysis. Finally, the study identified the synergistic migration mechanism of NH₄⁺ and Cl⁻, as well as the activation trend of As in reducing environments. The uncertainty inherent to health risk assessment was considered by developing a kernel density estimation–trapezoidal fuzzy number–Monte Carlo simulation (KDE-TFN-MCSS) model that reduced the distribution mis-specification risks and high-risk misjudgment rates associated with conventional assessment methods. The results indicated that: (1) The water chemistry type in the study area was predominantly HCO₃⁻–Ca²⁺ with moderately to weakly alkaline water, and the primary and nitrogen pollution indicators were elevated, with the average NH₄⁺ concentration significantly increasing from 0.06 mg/L in 2014 to 1.26 mg/L in 2022, exceeding the Class III limit of 1.0 mg/L. (2) The groundwater quality in the central Songnen Plain was poor in 2014, comprising predominantly Classes IV and V; by 2022, it comprised mostly Classes I–IV following a banded distribution, but declined in some central and northern areas. (3) The results of the SOM analysis revealed that the principal hardness component shifted from Ca²⁺ in 2014 to Ca²⁺–Mg²⁺ synergy in 2022. Local high values of As and NH₄⁺ were determined to reflect geogenic origin and diffuse agricultural pollution, whereas the Cl⁻ distribution reflected the influence of de-icing agents and urbanization. (4) Through drinking water exposure, a deterministic evaluation conducted using the conventional four-step method indicated that the non-carcinogenic risk (HI) in the central and eastern areas significantly exceeded the threshold (HI > 1) in 2014, with the high-HI area expanding westward to the central and western regions in 2022; local areas in the north also exhibited carcinogenic risk (CR) values exceeding the threshold (CR > 0.0001). The results of a probabilistic evaluation conducted using the proposed simulation model indicated that, except for children’s CR in 2022, both HI and CR exceeded acceptable thresholds with 95% probability. Therefore, the proposed assessment method can provide a basis for improved groundwater pollution zoning and control decisions in cold regions. Full article

Evaluating the efficacy of available technology for pollutant treatment is critical for formulating environmental management policies and standards. To address the lack of systematic quantitative methods for evaluating available technology, we propose a method based on the Entropy Weight TOPSIS model which integrates technology, economic efficiency, environmental benefits, and operational feasibility. We applied this approach to evaluate six heavy metal wastewater treatment technologies used in the lead–zinc smelting industry in the Yellow River Basin of China. A total of 4 primary and 16 secondary evaluation indicators were identified. The data were mainly composed of supervised monitoring data collected by local environmental protection authorities and self-monitoring operation data collected from factories; moreover, 10 relevant experts were invited to assess the scoring indicators. The results showed that technical performance had the greatest contribution to the overall efficacy of the treatment technology (62.31% weight), followed by environmental benefits (14.24% weight), economic costs (12.08% weight), and operational feasibility (11.36% weight). The final scores and rankings of the six technologies evaluated showed that a sulfurization precipitation with two-stage lime neutralization and sedimentation technology received the highest score due to its balanced technical performance, economic cost, environmental benefits, and operational feasibility. Conversely, lime neutralization with flocculation precipitation technology ranked lowest due to its non-compliance with the emission limits in China, despite its low economic cost and carbon emission intensity. This study provides a quantitative methodological framework for evaluating available technology, emphasizing the balance of the technical, economic, and environmental benefits of the pollutant treatment technologies chosen and the relevant policies made. Full article

In this study, we compare two novel hybrid data assimilation (DA) methods: Localized Weighted Ensemble Kalman filter (LWEnKF) and Implicit Equal-Weights Variational Particle Smoother (IEWVPS). These methods integrate a particle filter (PF) with traditional DA methods. LWEnKF combines the PF with EnKF, while IEWVPS integrates the PF with the four-dimensional variational (4DVAR) method. These hybrid DA methods not only overcome the limitations of linear or Gaussian assumptions in traditional assimilation methods but also address the issue of filter degeneracy in high-dimensional models encountered by pure PFs. Using the Regional Ocean Model System (ROMS), the effects of different DA methods for mesoscale eddies in the northern South China Sea (SCS) are examined using simulation experiments. The hybrid DA methods outperform the linear deterministic variational and Kalman filter methods: compared to the control experiment (no assimilation), EnKF, LWEnKF, IS4DVar and IEWVPS reduce the sea level anomaly (SLA) root-mean-squared error (RMSE) by 55%, 65%, 65% and 80%, respectively, and reduce the sea surface temperature (SST) RMSE by 77%, 78%, 74% and 82%, respectively. In the short-term assimilation experiment, IEWVPS exhibits superior performance and greater stability compared to 4DVAR, and LWEnKF outperforms EnKF (LWEnKF’s posterior SLA RMSE is 0.03 m, lower than EnKF’s value of 0.04 m). Long-term forecasting experiments (16 days, starting on 20 July 2017) are also conducted for mesoscale eddy prediction. The variational methods (especially IEWVPS) perform better in simulating the flow field characteristics of eddies (maintaining accurate eddy structure for the first 10 days, with an average SLA RMSE of 0.05 m in the studied AE1 eddy region), while the filters are more advantageous in determining the total root-mean-squared error (RMSE), as well as the temperature under the sea surface. Overall, compared to EnKF and 4DVAR, the hybrid DA methods better predict mesoscale eddies across both short- and long-term timescales. Although the computational costs of hybrid DA are higher, they are still acceptable: specifically, IEWVPS takes approximately 907 s for a single assimilation cycle, whereas LWEnKF only takes 24 s, and its assimilation accuracy in the later stage can approach that of IEWVPS. Given the computational demands arising from increased model resolution, these hybrid DA methods have great potential for future applications. Full article

To improve the physical consistency and interpretability of traditional drought indices, this study proposes a drought assessment model that couples physically based constraints with data-driven approaches, leading to the development of a Multivariate Drought Index (MDI). The model employs convolutional neural networks to achieve physically consistent downscaling, thereby obtaining a high-resolution Normalized Difference Water Index (NDWI), Temperature Vegetation Dryness Index (TVDI), Vegetation Condition Index (VCI), and Temperature Condition Index (TCI). Objective weights are determined using the Criteria Importance Through Intercriteria Correlation method, while random forest and Shapley Additive Explanations are integrated for nonlinear interpretation and physics-guided calibration, forming an ensemble framework that incorporates multi-source and multi-scale factors. Validation with multi-source data from 2000 to 2024 in the major maize-growing areas of Heilongjiang Province demonstrates that MDI outperforms single indices and the Vegetation Health Index (VHI), achieving a correlation coefficient (r = 0.87), coefficient of determination (R² = 0.87), RMSE (0.08), and classification accuracy (87.4%). During representative drought events, MDI identifies signals 16–20 days earlier than the Standardized Precipitation Evapotranspiration Index (SPEI) and the Soil Moisture Condition Index (SMCI), and effectively captures localized drought patches at a 250 m scale. Feature importance analysis indicates that the NDWI and TVDI are consistently identified as dominant factors across all three methods, aligning physically interpretable analysis with statistical contribution. Long-term risk zoning reveals that the central–western region of the study area constitutes a high-risk zone, accounting for 42.6% of the total. This study overcomes the limitations of single indices by integrating physical consistency with the advantages of data-driven methods, achieving refined spatiotemporal characterization and enhanced overall performance, while also demonstrating potential for application across different crops and regions. Full article