Search Results (1,806)

With accelerating urbanisation, extreme rainfall events have become increasingly frequent, leading to rising urban flooding risks that threaten city operation and infrastructure safety. The rapid expansion of impervious surfaces reduces infiltration capacity and accelerates runoff responses, making cities more vulnerable to short-duration, high-intensity storms. Although the SWMM is widely used for urban stormwater simulation, its application is often constrained by the lack of detailed drainage network data, such as pipe diameters, slopes, and node connectivity. To address this limitation, this study focuses on the main built-up area within the Second Ring Expressway of Chengdu, Sichuan Province, in southwestern China. As a regional core city, Chengdu frequently experiences intense short-duration rainfall during the rainy season, and the coexistence of rapid urbanisation with ageing drainage infrastructure further elevates flood risk. Accordingly, a technical framework of “open road data substitution–automated modelling–SWMM-based assessment” is proposed. Leveraging the spatial correspondence between road layouts and drainage pathways, open road data are used to construct a virtual drainage system. Combined with DEM and land-use data, Python-based automation enables sub-catchment delineation, parameter extraction, and network topology generation, achieving efficient large-scale modelling. Design storms of multiple return periods are generated based on Chengdu’s revised rainfall intensity formula, while socioeconomic indicators such as population density and infrastructure exposure are normalised and weighted using the entropy method to develop a comprehensive flood-risk assessment. Results indicate that the virtual drainage network effectively compensates for missing pipe data at the macro scale, and high-risk zones are mainly concentrated in densely populated and highly urbanised older districts. Overall, the proposed method successfully captures urban flood-risk patterns under data-scarce conditions and provides a practical approach for large-city flood-risk management. Full article

(1) Background: Under the dual pressures of global climate change and rapid urbanization, blue–green infrastructure as a nature-based solution is crucial for enhancing urban sustainability. However, there is still a significant cognitive gap regarding the synergy mechanism between its blue and green components and its nonlinear combined impact on sustainability. (2) Method: To fill this gap, this study takes Zhenjiang, a national sponge pilot city in China, as a case and constructs a comprehensive assessment framework. The framework combines multi-source spatio-temporal big data (remote sensing images, point of interest data, mobile phone signaling data) with spatial analysis techniques (geodetectors, Getis-Ord Gi*) to quantify the synergistic effects of blue–green infrastructure on environmental, economic, and social sustainability. (3) Results: The main findings include the following: (1) urban sustainability presents a spatial differentiation pattern of “high in the center, low in the periphery, and multi-core”, and there is a significant positive spatial correlation with the distribution of blue–green infrastructure. (2) The economic dimension, especially daytime population vitality, contributes the most to overall sustainability. (3) Crucially, the co-configuration of sponge facility density and park facility density was identified as the most influential driving mechanism (q = 0.698). In addition, the interaction between the blue infrastructure and the green sponge facilities showed obvious nonlinear enhancement characteristics. Based on spatial matching analysis, the study area was divided into three priority intervention zones: high, medium, and low. (4) Conclusions: This study confirms that it is crucial to view blue–green infrastructure as an interrelated collaborative system. The findings deepen the theoretical understanding of the synergistic empowerment mechanism of blue–green infrastructure and provide scientifically based and actionable policy support for the precise planning of ecological spaces in high-density urbanized areas. Full article

The development of urban green infrastructures (UGI) is considered among the main nature-based solutions for climate mitigation in cities; however, the role of soils in the carbon (C) balance of UGI ecosystems remains largely overlooked. Urban green spaces are typically dominated by constructed Technosols, created by adding organic materials on top of former natural or agricultural subsoils. The combined effects of land-use history and current UGI management result in a high spatial variation of soil organic carbon (SOC) stocks and soil CO₂ emissions. Our study aimed to explore this variation for the case of Wageningen University campus. Developed on a former agricultural land, the campus area includes green spaces dominated by trees, shrubs, lawns, and herbs, with well-documented management practices for each vegetation type. Across the campus area (~32 ha), a random stratified topsoil sampling (n = 90) was conducted to map the spatial variation of topsoil (0–10 cm) SOC stocks. At the key sites (n = 8), representing different vegetation types and time of development (old, intermediate, and recent), SOC profile distribution was analyzed including SOC fractionation in surface and subsequent horizons, as well as the dynamics in soil CO₂ emissions, temperature, and moisture. Topsoil SOC contents on campus ranged from 1.1 to 5.5% (95% confidence interval). On average, SOC stocks under trees and shrubs were 10–15% higher than those under lawns and herbs. The highest CO₂ emissions were observed from soil under lawns and coincided with a high proportion of labile SOC fraction. Temporal dynamics in soil CO₂ emissions were mainly driven by soil temperature, with the strongest relation (R² = 0.71–0.88) observed for lawns. Extrapolating this relationship to the calendar year and across the campus area using high-resolution remote sensing data on surface temperatures resulted in a map of the CO₂ emissions/SOC stocks ratio, used as a spatial proxy for C turnover. Areas dominated by recent and intermediate lawns emerged as hotspots of rapid C turnover, highlighting important differences in the role of various UGI types in the C balance of urban green spaces. Full article

Rapid urbanization and persistent air pollution threaten the functional resilience of megacities in Southeast Asia, particularly Bangkok, where PM_2.5 concentrations consistently exceed World Health Organization (WHO) guidelines. To strengthen urban adaptive capacity, this study investigates the role of Nature-based Solutions (NbS), particularly urban green spaces, as resilience-oriented infrastructure for air quality management. Using data from 32 monitoring stations across the Bangkok Metropolitan Administration (BMA) and surrounding areas from 2021 to 2023, spatial and temporal trends in PM_2.5 concentrations were analyzed through geostatistical modeling and inferential statistics. Although all sites exceeded the WHO PM_2.5 guideline of 5 µg/m³, larger and more connected green spaces consistently exhibited better air-quality than the surrounding non-green urban mosaic. Areas with extensive vegetation, greater canopy cover, and more compact park geometries (lower perimeter-to-area ratios) demonstrated improved pollution attenuation capacity, while fragmented parks are more exposed to surrounding emissions. Integration of Local Climate Zone (LCZ) classification further indicated that compact high-rise zones and high-traffic corridors exhibited higher PM_2.5 levels due to reduced airflow and structural confinement. The study underscores the need to embed NbS within resilience-based urban planning to promote long-term environmental stability and public health recovery in rapidly urbanizing megacities like Bangkok. Full article

The growing availability of spatial data from remote sensing, laser scanning (LiDAR), and photogrammetric techniques stimulates the dynamic development of methods for the automatic detection and classification of topographic objects. In recent years, both classical machine learning (ML) algorithms and deep learning (DL) methods have found wide application in the analysis of large and complex data sets. Despite significant achievements, literature on the subject remains scattered, and a comprehensive review that systematically compares algorithm classes with respect to data modality, performance, and application context is still needed. The aim of this article is to provide a critical analysis of the current state of research on the use of ML and DL algorithms in the detection and classification of topographic objects. The theoretical foundations of selected methods, their applications to various data sources, and the accuracy and computational requirements reported in the literature are presented. Attention is paid to comparing classical ML algorithms (including SVM, RF, KNN) with modern deep architectures (CNN, U-Net, ResNet), with respect to different data types such as satellite imagery, aerial orthophotos, and LiDAR point clouds, indicating their effectiveness in the context of cartographic and elevation data. The article also discusses the main challenges related to data availability, model interpretability, and computational costs, and points to promising directions for further research. The summary of the results shows that DL methods are frequently reported to achieve several to over ten percentage points higher segmentation and classification accuracy than classical ML approaches, depending on data type and object complexity, particularly in the analysis of raster data and LiDAR point clouds. The conclusions emphasize the practical significance of these methods for spatial planning, infrastructure monitoring, and environmental management, as well as their potential in the automation of topographic analysis. Full article

Quantifying e-waste in Sub-Saharan Africa remains constrained by scarce data, weak institutional reporting, and the dominance of informal sector activity. We present the first nationwide assessment of e-waste generation and Random Forest-based national forecasting in Sierra Leone. A mixed-methods survey administered 6000 questionnaires across all 16 districts, targeting households, institutions, enterprises, and informal actors. The study documented devices in use, storage, and disposal across the following six categories: ICT, appliances, lighting, batteries, medical, and other electronics. Population growth and device adoption simulations were combined with lifespan distributions and a Random Forest model trained on survey and simulated historical data to construct e-waste flows and forecast quantities through to 2050, including disposal fate probabilities for repurposing versus discarding. The results showed sharp spatial disparities, with Western Urban (Freetown) averaging about 10 kg per capita compared to 1.8 kg per capita in rural areas. Long-term district patterns were highly concentrated: 50-year annual averages indicated that Western Area Urban contributes 15.3% of national totals, followed by Bo (12.7%) and Western Area Rural (12.1%), with the top five districts contributing 59.1%. By 2050, total national e-waste entering reuse and disposal pathways was projected to reach 23.4 kilo tons per year (kt yr⁻¹) with a 95% uncertainty interval (UI) of 11–42 kt yr⁻¹ (and a 99% interval extending to 50 kt yr⁻¹), corresponding to 0.9–3.4 kg/capita/year. Household appliances dominated total mass, ICT devices exhibited high reuse rates, and batteries showed minimal reuse despite high hazard potential. These findings provide critical evidence for e-waste policy, regulation, and infrastructure planning in data-scarce regions. Full article

Urban intersections are critical nodes for roadway safety, congestion management, and autonomous vehicle coordination. Traditional traffic control systems based on fixed-time signals and static sensors lack adaptability to real-time risks such as red-light violations, near-miss incidents, and multimodal conflicts. This study presents a grid-enabled framework integrating computer vision and machine learning to enhance real-time intersection intelligence and road safety. The system overlays a computational grid on the roadway, processes live video feeds, and extracts dynamic parameters including vehicle trajectories, deceleration patterns, and queue evolution. A novel active learning module improves detection accuracy under low visibility and occlusion, reducing false alarms in collision and violation detection. Designed for edge-computing environments, the framework interfaces with signal controllers to enable adaptive signal timing, proactive collision avoidance, and emergency vehicle prioritization. Case studies from multiple intersections typical of US cities show improved phase utilization, reduced intersection conflicts, and enhanced throughput. A grid-based heatmap visualization highlights spatial risk zones, supporting data-driven decision-making. The proposed framework bridges static infrastructure and intelligent mobility systems, advancing safer, smarter, and more connected roadway operations. Full article

This study presents a machine learning framework to automate the mapping of the Integrated Relative Anthropization Index (INRA, by its Spanish acronym). A predictive model was developed to estimate the degree of anthropization in the basin of Lake Tota, Colombia, using the XGBoost machine learning algorithm and remote sensing data. This research, part of a broader wetland monitoring project, aimed to identify the optimal spatial scale for analysis and the most influential predictor variables. Methodologically, models were tested at resolutions from 20 m to 500 m. The results indicate that a 50 m spatial scale provides the optimal balance between predictive accuracy and computational efficiency, achieving robust performance in identifying highly anthropized areas (sensitivity: 0.83, balanced accuracy: 0.91). SHAP analysis identified proximity to infrastructure and specific Sentinel-2 spectral bands as the most influential predictors in the INRA emulation model. The main result is a robust, replicable model that produces a detailed anthropization map, serving as a practical tool for monitoring human impact and supporting sustainable management strategies in threatened high-Andean ecosystems. Rather than a simple classification exercise, this approach serves to deconstruct the INRA methodology, using SHAP analysis to reveal the latent non-linear relationships between spectral variables and human impact, providing a transferable and explainable monitoring tool. Full article

Flooding episodes caused by a heavy rainfall event have become more frequent, especially during the rainfall season in Botswana, which poses some socio-economic and environmental risks. This study investigates the capability of the Weather Research and Forecasting (WRF) model in simulating a heavy rainfall event that occurred on 26 December 2023 in Mahalapye District, Botswana. This event is one among many that have negatively impacted the lives and infrastructures in Botswana. The WRF model was configured using the tropical-suite physics schemes, i.e., (Rapid Radiative Transfer Model, Yonsei University planetary boundary layer scheme, Unified Noah land surface model, New Tiedtke, Weather Research and Forecasting Single-Moment six-class) on a two-way nested domain (9 km and 3 km grid spacing) and was initialized with the GFS dataset. Gauged station data was used for verification alongside synoptic charts generated using ECMWF ERA5 dataset. The results show that the WRF model simulation using the tropical-suite physics schemes is able to reproduce the spatial and temporal patterns of the observed rainfall but with some notable biases. Performance metrics, including RMSE, correlation coefficient, and KGE, showed moderate to good agreement, highlighting the model’s sensitivity to physical parameterization and resolution. The results of this study conclude that the WRF model demonstrates promising potential in forecasting extreme rainfall events in Botswana, but more sensitivity tests to different parameterization schemes are needed in order to integrate the model into the early warning systems to enhance disaster preparedness and response. Full article

As an important industrial organizational form in the era of the digital economy, digital industry agglomeration exerts a profound impact on urban ecological resilience. Using panel data of 281 prefecture-level cities in China from 2011 to 2021, this study measures the level of digital industry agglomeration by means of the location entropy method, and constructs an urban ecological resilience evaluation system based on the “Pressure-State-Response (PSR)” model. It systematically examines the impact effects and action mechanisms of digital industry agglomeration on urban ecological resilience. The results show that: (1) The spatio-temporal evolution of the two presents a gradient pattern of “eastern leadership and central-western catch-up”, and their spatial correlation deepens over time, with the synergy maturity in the eastern region being significantly higher than that in the central and western regions. (2) Digital industry agglomeration significantly promotes the improvement in urban ecological resilience, and this conclusion remains valid after endogeneity treatment and robustness tests. (3) The promotional effect is more prominent in central cities, coastal cities, and key environmental protection cities, whose advantages stem from digital infrastructure and innovation endowments, industrial synergy and an open environment, and the adaptability of green technologies under strict environmental regulations, respectively. (4) Digital industry agglomeration empowers ecological resilience by driving green innovation and improving the efficiency of land resource allocation, while the construction of digital infrastructure plays a positive regulatory role. Full article

Rapid urbanisation in coastal megacities intensifies coupled climate and water-related challenges, including heat stress, ventilation deficits, and increasing sensitivity to hydro-climatic extremes. Urban morphology plays a critical role in regulating these climate–water interactions by shaping airflow, surface heat exchange, and the spatial organisation of green–blue infrastructures. This study develops a Local Climate Zone (LCZ)-conditioned generative modelling framework based on a Conditional Pix2Pix Generative Adversarial Network, using paired LCZ classification maps and urban morphology data derived from six representative cities in the Guangdong–Hong Kong–Macao Greater Bay Area: Guangzhou, Shenzhen, Hong Kong, Macao, Zhuhai, and Dongguan. By integrating remote sensing–derived LCZ classifications with urban morphology data, the proposed framework learns spatial patterns associated with key morphology-related predictors, including building density and compactness, height-related structural intensity, open-space distribution, and the continuity of green–blue and ventilation corridors. The model demonstrates robust performance (SSIM = 0.74, R² = 0.81, PSNR = 15.3 dB) and strong cross-city transferability, accurately reproducing density transitions, ventilation corridors, and green–blue spatial structures relevant to coastal climate and water adaptation. The results highlight the potential of LCZ-informed generative modelling as a scalable decision-support tool for climate–water adaptive urban planning, enabling rapid exploration of morphology configurations that support heat mitigation, ventilation enhancement, and resilient coastal transformation. Full article

Rice is a vital staple food crop worldwide and also one of the major sources of greenhouse gas (GHG) emissions, generating substantial methane (CH₄) and nitrous oxide (N₂O). As one of the key management practices for rice production, the GHG mitigation potential of water management has attracted extensive attention, whereas its global scalability remains to be further investigated. Based on 15,458 global observations of field experimental data, we employed advanced machine learning methods to quantify the GHGs and soil carbon sequestration of global rice systems around 2020. Furthermore, we identified the optimal spatial distribution of GHG mitigation for five rice water management practices (continuous flooding (CF), flooding–midseason drainage–reflooding (FDF), alternate wetting and drying irrigation (AWD), flooding–midseason drainage–intermittent irrigation (FDI), and rainfed cultivation (RF)) through scenario simulation, under the premise of no yield reduction. The results of machine learning simulation showed that optimizing water management could reduce global rice greenhouse gas emissions by 39.17%, equivalent to 340.46 Mt CO₂ eq, while increasing rice yields by 3.55%. This study provides valuable insights for the optimization of agricultural infrastructure and the realization of agricultural sustainable development. Full article

This study presents a robust framework for optimizing the site selection of Electric Vehicle Charging Stations (EVCS) in Qatar by integrating a Geographic Information System (GIS) with a Multi-Criteria Decision-Making (MCDM) model. The core innovation lies in the enhancement of the conventional Analytic Hierarchy Process (AHP) with a Removal Sensitivity Analysis (RSA). This unique integration moves beyond traditional, subjective expert-based weighting by introducing a transparent, data-driven methodology to quantify the influence of each criterion and generate objective weights. The Analytic Hierarchy Process (AHP) was used to evaluate fourteen criteria related to accessibility, economic and environmental factors that influence EVCS site suitability. To enhance robustness and minimize subjectivity, a Removal Sensitivity Analysis (RSA) was applied to quantify the influence of each criterion and generate objective, data-driven weights. The results reveal that accessibility factors, particularly proximity to road networks and parking areas exert the highest influence, while environmental variables such as slope, CO concentration, and green areas have moderate but spatially significant impacts. The integration of AHP and RSA produced a more balanced and environmentally credible suitability map, reducing overestimation of urban sites and promoting sustainable spatial planning. Environmentally, the proposed framework supports Qatar’s transition toward low-carbon mobility by encouraging the expansion of clean electric transport infrastructure, reducing greenhouse gas emissions, and improving urban air quality. The findings contribute to achieving the objectives of Qatar National Vision 2030 and align with global efforts to mitigate climate change through sustainable transportation development. Full article

Community public sport facilities are core carriers of the national fitness public service system, with their supply–demand alignment directly linked to megacity governance efficiency and residents’ well-being. To address structural issues, such as “human–land imbalance” in facility layout, this study uses the 2010–2024 panel data from Shanghai’s 16 districts, applies supply–demand equilibrium theory, and integrates quantitative methods to analyze spatio-temporal supply–demand coupling and identify key influencing factors. The study yields four key findings: (1) The spatial distribution of facilities and population demonstrates a differentiated evolutionary trajectory marked by “central dispersion and suburban stability”. (2) Supply–demand alignment has continuously improved, as evidenced by the increase in coordinated administrative districts from six to thirteen. Nonetheless, the distance between sports facilities and population centers widened, suggesting that spatial adaptation remains incomplete. (3) Urban population growth exerts a significant positive impact on facility supply. Elasticity coefficients are generally high in suburban areas, while negative elasticity is detected in some central urban areas due to population outflow. (4) Facility construction intensity and residential activity intensity are core driving factors, with economic conditions, transportation infrastructure, and housing prices acting as key supporting factors. This study overcomes traditional aggregate-quantity research limitations, reveals megacity facility supply–demand “spatial mismatch” dynamics, and provides a scientific basis for targeted public sports facility layout and refined governance. Full article

The subgrade is a critical component of highway infrastructure that directly affects pavement performance and traffic safety. With the rapid expansion of highway networks and increasing heavy-truck traffic, latent subgrade distresses, such as insufficient base strength, uneven settlement, and base cracking, have become key factors limiting pavement serviceability. These distresses are often difficult to detect at early stages and may evolve into sudden structural failures if not properly identified. This study investigates the evolution mechanisms and spatial characteristics of representative subgrade distresses through an integrated framework combining FWD screening, GPR imaging, core sampling, and Abaqus-based finite element simulation. Field data were collected from the Changshen Expressway. Potential weak zones were first identified using FWD testing and further localized by GPR, while multilayer constitutive parameters were obtained from core sample analyses. The field-derived material parameters were then incorporated into an FE model to simulate pavement responses under loading and to interpret the underlying distress mechanisms. The proposed framework enables identification of dominant distress types, quantification of stiffness degradation, and clarification of deterioration pathways within the subgrade system. The results provide practical support for condition assessment, health monitoring, and maintenance decision-making in highway infrastructure. Full article