Search Results (173)

The extensive use of high-resolution digital elevation data, along with continuous improvements in computing power and geographic information system (GIS) tools. Has driven the development of spatial data processing, management, and spatial interpolation methods. This study aims to construct a high-quality spatial distribution map of thermal comfort in densely populated areas of northwestern Jordan using climate data collected from six meteorological stations between 1991 and 2024, based on the indoor temperature index (IAT). To analyze the spatial variability of climate elements, a digital elevation model (DEM) with a spatial resolution of 30 m was used and resampled to a 0.5-km grid. Spatial interpolation employed inverse distance weighting (IDW), with each grid cell using data from the three nearest meteorological stations. The results showed that areas with higher temperatures inside the villas were clearly concentrated in the summer, especially in the lowlands near the Jordan Valley. Indicating that these areas are more susceptible to thermal stress. The model results also show that it performs well in predicting thermal comfort, with a coefficient of determination (R²) between 0.95 and 0.98 and mean squared error (MSE) between 0.35 and 0.50. which reflects the ability of these models to represent the relationship between climate variables and predict thermal comfort levels with a high degree of accuracy. The results indicate significant spatiotemporal differences in thermal comfort within the study area, with longer durations of heat stress in summer. This highlights the importance of combining geospatial methods with numerical simulations in studying the impacts of climate change and supporting urban planning and climate adaptation strategies. Full article

In fluid analysis of stent models with a braided structure, conventional modeling using Finite Element Method (FEM) boundaries requires extremely fine fluid mesh resolution. The objective of this study is to propose a grid size design strategy for FEM–Discrete Element Method (DEM) coupled analysis. In the proposed method, the fluid is modeled using FEM, while the stent is modeled using a continuous arrangement of DEM particles. The volume-force-based coupling method eliminates the need for node sharing between the FEM and DEM, thereby reducing the modeling workload. In this study, we derived grid sizes based on flow analysis around a single strand and verified the flow analysis around a 3D braided stent model. The results showed that the proposed method reduced the number of fluid grids by approximately 44% compared to conventional methods. In this case, the maximum errors in velocity and pressure were 0.0064 m/s and 17.07 Pa, respectively, and high correlations of 0.9 or higher were obtained for both distributions. Furthermore, the maximum relative error in the drag coefficient was 4.332%. This study provides guidelines for a fluid grid size design method that enables the reduction in computational cost and modeling burden in fluid flow analysis around stents. Full article

Regional development disparities remain a persistent global challenge, yet existing assessment approaches often face a trade-off between spatial detail and temporal coverage. Conventional socioeconomic statistics provide relatively reliable information but are typically limited by coarse spatial representation and low update frequency, whereas high-resolution remote sensing-based studies often focus on individual time points, making it difficult to capture the temporal evolution of regional development. Remote sensing observations provide valuable opportunities for regional development assessment by offering extensive spatial coverage and repeated observations over time. To address this gap, this study proposes a multisource remote sensing framework for characterizing the spatiotemporal dynamics of regional development in Chongqing Municipality across four temporal nodes (2014, 2016, 2018, and 2020). We first construct a county-level Development Intensity Index (DII) using socioeconomic indicators derived from statistical data. Subsequently, we integrate nighttime light, DEM, NDVI, and POI data to generate a 500 m gridded Comprehensive Spatial Development Index (CSDI), which captures spatial heterogeneity at a fine spatial scale. The CSDI exhibits strong correspondence with the DII, and its spatial validity is further corroborated through visual interpretation of Google Earth imagery. Results indicate that areas with higher development levels are predominantly concentrated in Chongqing’s central urban core, while less-developed counties are concentrated in the northeastern and southeastern peripheries. Although a general upward trend in development is observed across the study period, notable spatial disparities persist. Overall, the proposed CSDI-based framework offers an effective and replicable approach for gridded regional development assessment, with implications for targeted regional planning and differentiated policy design. Full article

Drought is a recurrent hazard in the Chi River Basin, northeastern Thailand, causing agricultural losses despite its low-lying floodplain setting. This study developed a machine learning framework integrating Sentinel-2 indices (SMI, NDVI, MNDWI, VCI, NDMI) and static spatial variables (DEM, TWI, prox2river, slope) aggregated to H3 hexagonal grids (≈5.96 km²) for village-relevant analysis. Drought reports from 1482 observations (2019–2024) were aggregated to 203 grid cells. Three models—Random Forest, XGBoost, and LightGBM—were evaluated using temporal (training: 2019–2023; test: 2024) and spatial holdout validation. LightGBM achieved the best performance with AUC = 0.783 (temporal) and 0.714 (spatial), accuracy = 78.3%, and balanced accuracy = 76.4%. Five-class severity classification showed declining accuracy from 71.4% (Very Low) to 25.0% (Severe), limited by rare event sample sizes. SHAP analysis revealed static topographic variables dominated importance (76.1%) over remote sensing indices (23.9%), with weak individual correlations (|r| < 0.10). The framework is best characterized as a drought risk mapping tool for identifying persistently vulnerable areas rather than an operational early warning system. The methodology is transferable to similar floodplain environments with local re-estimation and validation. Full article

The black soil region of Northeast China is a key grain-production area where cropland water erosion threatens soil fertility and sustainability. We diagnosed cropland soil loss across six diagnostic years/time slices (2001, 2005, 2010, 2015, 2020, and 2024) using a Revised Universal Soil Loss Equation (RUSLE)-based remote-sensing workflow implemented in Google Earth Engine (GEE). Rainfall erosivity was derived from Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) daily precipitation, soil erodibility from SoilGrids, topography from the Shuttle Radar Topography Mission digital elevation model (SRTM DEM), vegetation cover from the Landsat normalized difference vegetation index (NDVI), and cropland extent from ESA WorldCover; alternative rainfall sources, cropland masks, and P-factor settings were used for sensitivity analyses. Under the slope-graded P-factor scenario, mean annual soil loss ranged from 1.60 to 3.07 t ha⁻¹ yr⁻¹, and the proportion of cropland exceeding T = 2 t ha⁻¹ yr⁻¹ ranged from 25.2% to 52.9%. Soil loss fluctuated among years because rainfall erosivity and cover-management effects partly counteracted each other. Risk was concentrated in sloping piedmont and hilly cropland, whereas broad plains were dominated by very slight and slight erosion. P-factor parameterization represented the largest structural uncertainty. The workflow provides regional screening evidence for field verification and conservation-practice assessment, rather than direct site-specific engineering prescriptions. Full article

Urban heat and walkability are often assessed separately, although pedestrians experience street connectivity, topography, vegetation, and thermal exposure simultaneously. This study develops a reproducible open-data workflow to examine the spatial relationship between walkability potential and surface thermal pressure in Loja, Ecuador, an intermediate Andean city shaped by valley morphology, steep slopes, uneven urban expansion, and heterogeneous green-space distribution. The analysis combines Landsat-derived land surface temperature, OpenStreetMap urban-form indicators, DEM-derived slope, composite spatial indicators, and spatial autocorrelation across 100 m, 250 m, and 500 m grids. The results show that walkability potential, observed LST-based heat intensity, and walkability–heat balance is spatially structured rather than randomly distributed. At the 250 m scale, heat-intensity clusters were spatially selective, indicating that surface thermal pressure is concentrated in specific parts of the retained analytical grid rather than uniformly distributed across the city. The 250 m grid provided the most interpretable balance between local detail and spatial stability for this case study, while the 100 m and 500 m grids revealed the sensitivity of the results to spatial aggregation. The study does not measure physiological thermal comfort or pedestrian heat stress. Instead, it offers an exploratory diagnostic framework for identifying where pedestrian-supportive urban form and surface thermal pressure overlap or diverge. This approach can help data-constrained intermediate cities prioritize areas for field verification, shade assessment, green-infrastructure planning, and more detailed pedestrian-level thermal studies. Full article

Urban flood simulation requires numerical models capable of representing the two-dimensional propagation of water over topographically complex surfaces under intense rainfall and localized inflows. The paper presents a two-dimensional hydraulic model based on the Saint-Venant equations (shallow-water equations). The numerical model uses a Rusanov-type flux and operates on raster-based digital elevation models (DEMs). Notably, it allows for the modeling of both spatially distributed rainfall over the domain (rain-on-grid approach) and multiple independent point sources representing urban drainage system surcharging or overflow, each associated with a specific discharge hydrograph. The results confirm the model’s reliability for urban hydraulic hazard assessments. Full article

Digital soil mapping (DSM), based on multimodal satellite data, is a crucial tool for the transition to precision agriculture. However, systematic studies using this method and machine and deep learning techniques are lacking for the arid and semi-arid regions of Central Asia, where multimodal satellite data can provide valuable insights into soil conditions. This work provides, for the first time, benchmark metrics for the predictive ability of six soil agrochemical properties (pH, Soil Organic Carbon, NO₃, P₂O₅, K₂O, and S) in the dry steppe zone of Central Asia, with a quantitative assessment of the difference between “standard” and “fair” validation strategies. This has methodological significance for the entire field of DSM research. A comprehensive comparison of 11 machine learning (ML) models and four deep learning (DL) architectures was conducted to predict soil agrochemical properties using a set of 530 features extracted from various satellite datasets. These features were extracted from Sentinel-2, Landsat-8, Sentinel-1 SAR, SRTM DEM, and ERA 5-Land using Google Earth Engine (GEE) automated pipeline. All models were evaluated using three spatial validation strategies with increasing stringency: Leave-One-Field-Out CV (LOFO-CV), Leave-One-Farm-Out CV (Farm-LOFO), and an optimized spatial split. We propose a three-level hierarchical validation scheme that allows for the quantitative separation of spatial leakage and feature selection leakage, a methodology that can be applied to any spatial ML problem. Local models have been shown to outperform the global SoilGrids v2.0 product in terms of accuracy, demonstrating the need for high-resolution regional models for precision agriculture. Local models outperformed SoilGrids v2.0 by 3.6× in Spearman ρ for pH (0.750 vs. 0.208), quantitatively confirming the necessity of regional calibration over global soil products. Multi-season ConvNeXt with SE-blocks on 54-channel composites improved R² for NO₃ by 36% (0.422 → 0.575), confirming the value of temporal dynamics for mobile elements; however, it underperformed RF on tabular features for most properties at the available sample size (n = 1085). Full article

Urban pluvial flood risk in industrial zones is intensifying under climate change, yet the joint influence of digital elevation model (DEM) resolution, surface roughness heterogeneity, and infiltration capacity on simulation accuracy remains insufficiently characterized. This study presents a comprehensive sensitivity analysis combining five DEM resolutions (0.5, 1, 2, 5, and 10 m), six rainfall scenarios (10- to 200-year return periods plus the observed event of 10 July 2024), and three infiltration rates (5, 10, and 20 mm h⁻¹), yielding 90 simulation cases executed with the open-source GPU solver SynxFlow on an NVIDIA A100 80GB GPU. A spatially distributed Manning’s roughness field (n_M = 0.013–0.100 s m^−1/3) was derived from the Ministry of Environment land cover product, replacing the conventional uniform-roughness assumption. Model performance was assessed against seven validation gauges (five flooded, two no-flood controls) compiled from contemporaneous news reports, using the 25 m × 25 m patch-maximum simulated depth at each gauge and probability of detection (POD), false alarm ratio (FAR), and critical success index (CSI). The 0.5 m baseline achieved POD = 0.80, FAR = 0.20, and CSI = 0.67 at the 5 cm depth threshold. Coarsening the grid reduced peak depth by up to 37% and flooded area by 5%, with the most rapid degradation occurring between 2 m and 5 m. A 2 m grid retained area error within 2% and volume error within 1% while delivering an approximately 33-fold runtime reduction relative to the 0.5 m baseline; the 10 m grid achieved up to ~1400× speedup, spanning three orders of magnitude across the resolution range. Resolution sensitivity intensified under higher rainfall and lower infiltration, confirming that “adequate” resolution is conditional on event severity. A tiered resolution selection matrix linking application scale, target accuracy, and computational cost is proposed to support evidence-based flood adaptation planning for industrial zones. Full article

Rough-walled fractures in conglomerate reservoirs promote near-wellbore proppant deposition, nonuniform flow, and insufficient distal support, making proppant-schedule screening difficult using small-scale smooth-slot tests alone. This study develops a benchmark-constrained and cost-aware hierarchical screening workflow by integrating a 20 m rough-wall physical experiment, transient Fluent simulations, and archived short-time EDEM sensitivity records. The benchmark experiment used a 20 m × 4.5 m × 10 mm artificial rough-wall fracture and ten operating conditions involving pumping rate, fluid viscosity, proppant size, and sand concentration. In the Fluent model, wall roughness was treated as a regularized roughness representation, and the carrier fluids were modeled using Newtonian constant viscosities measured from laboratory calibration. The experimental effective propped area ranged from 25.5% to 65.1%. Within single-factor comparison subsets, medium viscosity improved support continuity, pumping-rate gains became limited near 0.20 m³/min, particle size affected the balance between distal coverage and bed stability, and 300 kg/m³ sand concentration caused blockage. Image-segmentation-based comparison showed that Fluent captured the main wedge-shaped deposition morphology and screening-level geometric trends. The archived EDEM records indicated that grid-resolution refinement and mixed particle-size representation substantially increased computational cost. A Case 10 mesh-sensitivity check further confirmed that mesh refinement did not alter the first-order deposition morphology. The proposed workflow uses Fluent for whole-domain rapid screening and reserves EDEM/CFD-DEM for targeted short-time sensitivity checks. Full article

In biomass pellet combustion, the formation of ash layers on particle surfaces severely hinders combustion reactions and heat transfer, while the key parameters governing particle motion behavior and ash pre-separation in rotating cone burners remain insufficiently understood. To address these challenges and to optimize particle mixing and ash separation performance, this study adopts a combined numerical approach. The discrete element method (DEM) coupled with the Hertz–Mindlin (no-slip) contact model is employed to simulate particle motion and mixing dynamics, while a separate cold-state computational fluid dynamics (CFD) model based on the Realizable k-ε turbulence model and the discrete phase model (DPM) with Rosin–Rammler particle size distribution is established to investigate ash separation mechanisms. The Lacey mixing index is used to quantify mixing uniformity, and grid independence verification is performed to ensure numerical reliability. Key findings reveal that the rolling regime (rotational speed: 1.7–11 r/min), a uniform particle size of 25 mm, and a cone inclination angle of 45° collectively optimize particle mixing. Rotational speed is identified as the dominant factor affecting mixing effectiveness. Furthermore, an optimal secondary-to-primary air ratio of approximately 7:3 (within the tested range) balances enhanced centrifugal separation with flow field stability by mitigating backflow and excessive turbulence. This work not only fills the knowledge gap regarding the coupled effects of operational and structural parameters on particle behavior in rotating cone burners but also provides novel, quantitative guidance for the rational design and parameter tuning of such burners to improve combustion efficiency and operational stability. Full article

Fluid–structure interaction (FSI) research is crucial for applications in fields such as naval engineering, geological hazards, and biomechanics. Traditional grid-based methods (such as CFD) often face challenges in simulating large-deformation flow fields and complex boundary conditions, where mesh distortion can compromise simulation accuracy. Building upon the DualSPHysics5.2 framework, this study leverages the strengths of weakly compressible SPH (WCSPH) in modeling free surface flows and large-deformation fluids, as well as the discrete element method (DEM), for accurately describing particle collisions and fragmentation behaviors. We propose an improved MSPH-DEM coupling algorithm that incorporates moving least squares (MLS) correction for kernel function gradient optimization. This algorithm utilizes MLS-based gradient correction to achieve smoother fluid surfaces as well as bidirectional coupling between fluids and particles. Experimental validation demonstrates that in dam break simulations, this method reduces pressure errors. In the dam break impacting a cube experiment, it enhances accuracy, while in the dam break impacting a baffle experiment, the horizontal displacement of marker points closely aligns with the experimental values from Liao et al. This approach effectively improves the accuracy of the simulations of FSI problems, offering a more reliable numerical simulation methodology for engineering applications such as geological hazard prevention. Full article

Traditional linear models in urban informatics struggle to capture the complex, non-linear spatial non-stationarity inherent in metropolitan housing markets. To overcome these constraints, this study introduces a data-driven computational framework integrating a Spatio-Temporal Graph Convolutional Network (STGCN) with gradient-based Explainable Artificial Intelligence (XAI) and Geographically Weighted Regression (GWR). This framework is empirically tested using 217,598 apartment transactions in Busan, the Republic of Korea, augmented with high-resolution micro-demographic grids and Digital Elevation Model (DEM) topographical data. Utilizing unsupervised K-Means clustering, the region is spatially stratified into a dense Urban Core and a dispersed Suburban Periphery. The STGCN demonstrates overwhelming predictive superiority ($R^2=0.802$) over the traditional Spatial Error Model ($R^2=0.437$). Crucially, gradient-based XAI and localized GWR coefficients successfully unspool the deep learning “black box,” visualizing hyper-localized economic realities that global linear models obscure. The analysis expose stark regional market segmentation driven by environmental topography, mathematically quantifying non-linear dynamics such as coastal high-floor premiums, severe mountainous altitude penalties, and latent urban reconstruction premiums. Ultimately, this research bridges the gap between predictive computational power and spatial economic interpretability, offering a robust informatics framework for equitable urban planning. Full article

Accurate extraction of mountaintop areas from grid digital elevation models (DEMs) is essential for terrain analysis, geomorphological research, hydrological modeling, natural disaster monitoring, and emergency communication site selection. However, existing deep-learning-based methods often suffer from inadequate representation of local details and limited global contextual awareness, leading to blurred boundaries and reduced segmentation accuracy in complex mountainous terrains. To address these limitations, this study proposes a dual-encoder and global-context-enhanced TransUNet framework, named DEGC-TransUNet, for automated mountaintop delineation. The architecture integrates a convolutional encoder to capture fine-grained local terrain features and a MaxViT-based encoder to model multi-scale global context by encoding low-dimensional topographic attributes such as slope and curvature. A dedicated feature fusion module harmonizes complementary representations from both encoding paths, while a BiFormer-based strategy is introduced at the bottleneck to strengthen long-range dependencies and enhance convergence. The experimental results demonstrate that DEGC-TransUNet significantly outperforms baseline models such as TransUNet, DE-TransUNet, and GC-TransUNet, with relative improvements of 19.8% in Intersection over Union (IoU), 10.4% in overall accuracy (ACC), and 10.9% in F1-score. These findings provide a robust solution for mountaintop extraction, with significant potential in analyzing geomorphological evolution, simulating soil erosion, modeling species distribution in “sky island” ecosystems, and optimizing strategic placements for communication base stations and wind energy infrastructures. Full article

The widespread loess in western China poses significant challenges to transportation infrastructure construction due to its water sensitivity and collapsibility. This study investigates the interface mechanical properties of bamboo geogrid-reinforced loess under static loading through large-scale indoor pull-out tests and DEM–FDM coupled numerical simulations. The effects of vertical stress, the pull-out rate, the number of transverse ribs, burial depth, and reinforcement material on interface behavior were systematically evaluated. Results show that peak pull-out force increases with vertical stress, the number of transverse ribs, and burial depth, with all curves exhibiting pronounced strain hardening followed by softening characteristics. The pull-out rate exhibits a non-monotonic effect, with peak resistance higher at both lower and higher rates compared to intermediate rates. Bamboo geogrids demonstrate substantially superior performance over geogrids, with approximately four times higher peak pull-out resistance and greater initial stiffness. Numerical analysis reveals increased porosity and decreased coordination number in the grid vicinity, the horizontal stratification of the slip rate along the reinforcement, and concentration of strong force chains ahead of transverse ribs, elucidating the model-derived mechanisms underlying the macroscopic reinforcement effects. The findings confirm that bamboo geogrids provide effective and sustainable reinforcement for loess subgrades, offering a scientific basis for environmentally friendly engineering applications in loess regions. Although potential long-term durability under field environmental conditions requires further verification, the superior mechanical interface performance demonstrated here positions treated bamboo geogrids as a promising sustainable reinforcement option. Full article