Search Results (934)

This paper analyzes the implementation of the Geoportal SIG FESBAL–UPM, a Participatory Geographic Information System (PGIS) developed within the Master’s and Doctorate programs in Rural Development Project Planning and Sustainable Management at UPM. The study introduces a model integrated with Project-Based Learning (PBL), the Working With People (WWP) framework, and the CFS-RAI principles to address challenges in responsible food systems. The geoportal designed to be applied at the Food Bank–UPM Chair–FESBAL, acts as an innovative instrument for participation among the different stakeholders enabling the spatialization and analysis of data across social, environmental, and governance dimensions. Functionally, it offers a robust foundation for evidence-based decision-making, systematizes geographic information, and visualizes data via the web, supporting research, training, and community engagement actions. Furthermore, this study details the specific projects and activities developed under the three involved action lines: research, training, and community engagement, identifying strengths and weaknesses in each. The findings affirm that this participatory approach ensures that the proposed solutions are aligned with local needs and priorities, increasing the sustainability and long-term success of the projects implemented through the geoportal. Full article

Bike-sharing use has been growing because it improves personal mobility, offers an alternative to walking, and strengthens connections to transit. Demand forecasting is crucial for bike-sharing services because it enables operators to anticipate empty stations and full docks, improve vehicle rebalancing and staffing, and deliver more reliable service at lower operating cost. In this paper, we propose a cluster-based, hour-ahead demand forecasting methodology that (1) groups stations into geographically coherent areas using K-means clustering method, (2) constructs hourly arrival and departure demand time series for each cluster while explicitly preserving zero-demand hours, and (3) incorporates exogenous factors such as temperature and weather-event type. We analyze multi-year trip records from Chicago’s Divvy bike-sharing system (2014–2017) to characterize network expansion and assess spatial stability over time. We then use the period (1 August 2016–31 December 2017), during which the number of active stations is stable, to conduct our predictive modeling. We compare three machine learning-based predictive models—linear regression (LR), time series (TS), and random forest (RF)—and assess their out-of-sample performance using the root mean squared error (RMSE). Results show that TS and RF models consistently outperform LR, achieving up to 80% $R^2$ values and substantially lower RMSE across all 10 clusters, with particular improvements in high-variability central areas. By forecasting net demand (arrivals minus departures) at the cluster level, the approach supports practical identification of likely surplus/deficit areas to guide rebalancing decisions. 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

Soil erosion is a major threat to the sustainable productivity of arable lands, making the accurate prediction of soil erodibility essential for effective soil conservation planning. Soil erodibility is strongly controlled by intrinsic soil properties that regulate aggregate resistance and detachment processes under erosive forces. In this study, machine learning (ML) models, including the Multi-layer Perceptron Regressor (MLP), Random Forest (RF), Decision Tree (DT), and Extreme Gradient Boosting (XGBoost), were applied to predict the soil erodibility factor (K-factor). A comprehensive set of soil properties, including soil texture, clay ratio (CR), organic matter (OM), aggregate stability (AS), mean weight diameter (MWD), dispersion ratio (DR), modified clay ratio (MCR), and critical level of organic matter (CLOM), was analyzed using 110 soil samples collected from the northern part of Adana Province, Türkiye. The observed K-factor was calculated using the RUSLE equation, and ML-based predictions were spatially mapped using Geographic Information Systems (GISs). The mean K-factor values for arable, forest, and horticultural land uses were 0.065, 0.071, and 0.109 t h MJ⁻¹ mm⁻¹, respectively. Among the tested models, XGBoost showed the best predictive performance, with the lowest MAE (0.0051) and RMSE (0.0110) and the highest R² (0.9458). Furthermore, the XGBoost algorithm identified the CR as the most influential variable, closely followed by clay and MCR content. These results highlight the potential of ML-based approaches to support erosion risk assessment and soil management strategies at the regional scale. Full article

This article introduces the design and implementation of an Unmanned Surface Vehicle (USV), named “AquaMIB”, which introduces a novel and integrated approach for real-time and autonomous water quality monitoring in aquatic environments. The system integrates modular hardware and software, combining sensors for temperature, pH, conductivity, dissolved oxygen, and oxidation reduction potential with GPS, LiDAR, a digital compass, communication modules, and a dedicated power unit. Software components include Python on a Raspberry Pi for navigation and control, C on an Atmega 324P for sensing, C++ on an Arduino Uno for remote control, and C#/JavaScript for the web-based control center. Users assign task points, and the USV autonomously navigates, collects data, and transmits it via RESTful API. Field trials showed 96.5% navigation accuracy over 2.2 km, with 66% of task points reached within 3 m. A total of 120 measurements were processed in real time and visualized as GIS-based spatial maps. The system demonstrates a cost-effective, modular solution for aquatic monitoring. The system’s ability to generate real-time GIS maps enables immediate identification of environmental anomalies, transforming raw sensor data into an actionable decision-support tool for aquatic management. 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

Eutrophication remains a persistent challenge in the Romanian Black Sea coastal zone, driven by excess nutrient inputs from riverine and coastal sources and further intensified by climate change. This study assesses eutrophication dynamics and explores mitigation options using an integrated framework that combines in situ observations, satellite-derived chlorophyll a data, machine learning, and system dynamics modelling. Water samples collected during two field campaigns (2023–2024) were analyzed for nutrient concentrations and linked with chlorophyll a products from the Copernicus Marine Service. Random Forest analysis identified dissolved inorganic nitrogen, phosphate, salinity, and temperature as the most influential predictors of chlorophyll a distribution. A system dynamics model was subsequently used to explore relative ecosystem responses under multiple management scenarios, including nutrient reduction, enhanced zooplankton grazing, and combined interventions. Scenario-based simulations indicate that nutrient reduction alone produces a moderate decrease in chlorophyll a (45% relative to baseline conditions), while restoration of grazing pressure yields a comparable response. The strongest reduction is achieved under the combined scenario, which integrates nutrient reduction with biological control and lowers normalized chlorophyll a levels by approximately two thirds (71%) relative to baseline. In contrast, a bloom-favourable scenario results in a several-fold increase in chlorophyll a of 160%. Spatial analysis highlights persistent eutrophication hotspots near the Danube mouths and urban discharge areas. These results demonstrate that integrated strategies combining nutrient source control with ecological restoration are substantially more effective than single-measure interventions. The proposed framework provides a scenario-based decision-support tool for ecosystem-based management and supports progress toward achieving Good Environmental Status under the Marine Strategy Framework Directive. Full article

The optimization of historic district form, given the coordinated relationship between global urbanization and sustainable development, faces the core contradiction between preservation and development. Taking Shenyang’s Nanshi area as a case study, this study aimed to construct a sustainable urban form evaluation system comprising 7 dimensions and 23 indicators by integrating multi-source geographic Big Data. A combination of a weighting approach in rank-order analysis and the entropy weight method was adopted, followed by spatial quantitative analysis conducted based on ArcGIS. The results showed that the sustainability of the area exhibited significant spatial differentiation: historic blocks became high-value areas due to their “small blocks, dense road network” fabric and high functional mix. However, newly built residential areas were low-value zones, constrained by factors such as fragmented green spaces, single-functional land use, and other limitations. Road network density and functional mixing were identified as the primary driving factors, while green coverage rate served as a secondary factor. Based on these findings, a three-tier “element–structure–system” optimization strategy was proposed, providing quantitative decision support for the low-carbon renewal of high-density historic urban districts. Full article

Bananas underpin Uganda’s food security and rural economy, but productivity is declining due to emerging pests, diseases, and declining soil fertility. To address these challenges, hybrid stress-tolerant banana varieties (HBVs) have been developed and released, but their adoption remains uneven across the country. This study analyzes the spatial distribution and determinants of HBV adoption and intensity in Uganda, providing new insights to inform scaling strategies. A cross-sectional survey of 624 banana-farming households was conducted across 24 districts in both traditional and non-traditional banana-growing regions. Data were analyzed using descriptive statistics and a Tobit regression model to capture both the binary decision to adopt and the intensity of adoption, measured as the number of HBV mats planted. Results showed significant regional variation; adoption was highest in Northern Uganda (73.9%) and lowest in Central and Southwestern regions (≈24%). Education and land size positively influenced adoption, while reliance on planting materials from fellow farmers consistently reduced adoption intensity across all regions. Gender and household structure also shaped adoption patterns, with male and married farmers more likely to plant larger areas of HBVs. The findings highlight the need for regionally tailored interventions, including strengthening formal seed systems, enhancing farmer knowledge, and addressing gender gaps in technology access. Strengthening institutional seed channels and extension support can accelerate HBV scaling and contribute to resilient banana production in Uganda. Full article

Grasslands represent an essential resource for rural economies and for the provision of ecosystem services, yet they are increasingly affected by anthropogenic pressures, functional land-use changes, and institutional constraints. This study develops a geospatial decision-support framework for assessing grassland suitability in Hunedoara County, Romania, by integrating the Analytic Hierarchy Process (AHP) and Weighted Overlay Analysis (WOA) within a GIS environment. The assessment is based on nine criteria thematically grouped into three dimensions: (A) physical-geographical, including topographic suitability, climatic pressure, and hydrological risk exposure; (B) ecological and conservation-related, reflected by ecological conservation value, ecological carrying capacity, and the anthropic pressure index; and (C) socio-economic and functional, represented by spatial accessibility, recreational value, and policy support mechanisms. Suitability is defined as the integrated capacity of grasslands to sustain productive and multifunctional uses compatible with ecological conservation and the existing policy framework. Results indicate that 0.43% of the grassland area exhibits very high suitability (Class 1), 44.51% high suitability (Class 2), and 54.75% moderate suitability (Class 3), while unfavorable areas account for only 0.31% of the total (Class 4). The proposed methodology is reproducible and transferable, providing support for prioritizing management interventions, agri-environmental payments, and rural planning in mountainous and hilly regions. Full article

Cocoa production in West Africa supplies most of the global demand but is increasingly constrained by yield stagnation, soil degradation, disease pressure, and climate variability. This review examines how integrating regenerative agriculture (RA) with unmanned aerial systems (UAS) and artificial intelligence (AI) can support more precise and resilient cocoa management across heterogeneous smallholder landscapes. A PRISMA-guided systematic review of peer-reviewed literature published between 2000 and 2024 was conducted, yielding 49 core studies analyzed alongside supporting evidence. The synthesis evaluates regenerative agronomic outcomes, UAV-derived multispectral, thermal, and structural diagnostics, and AI-based analytical approaches for stress detection, yield estimation, and management zoning. Results indicate that regenerative practices consistently improve soil health and yield stability, while UAS data enhance spatial targeting of rehabilitation, shade management, and stress interventions. AI models further improve predictive capacity and decision relevance when aligned with data availability and institutional context, although performance varies across systems. Reported yield stabilization or improvement typically ranges from 12–30% under integrated approaches, with concurrent reductions in fertilizer and water inputs where spatial targeting is applied. The review concludes that effective scaling of RA–UAS–AI systems depends less on technical sophistication than on governance arrangements, extension integration, and cooperative service models, positioning these tools as enabling components rather than standalone solutions for sustainable cocoa intensification. Full article

This paper introduces an iceberg-based digital risk twin (DRT) framework for the health monitoring of complex engineering systems. The proposed model transforms multidimensional sensor and contextual data into a structured, interpretable three-dimensional geometry that captures both observable and latent risk components. Each monitored parameter is represented as a vertical geometric sheet whose height encodes a normalized risk level, producing an evolving iceberg structure in which the visible and submerged regions distinguish emergent anomalies from latent degradation. A formal mathematical formulation is developed, defining the mappings from the risk vector to geometric height functions, spatial layout, and surface composition. The resulting parametric representation provides both analytical tractability and intuitive visualization. A case study involving an aircraft fuel system demonstrates the capacity of the DRT to reveal dominant risk drivers, parameter asymmetries, and temporal trends not easily observable in traditional time-series analysis. The model is shown to integrate naturally into AI-enabled health management pipelines, providing an interpretable intermediary layer between raw data streams and advanced diagnostic or predictive algorithms. Owing to its modular structure and domain-agnostic formulation, the DRT approach is applicable beyond aviation, including power grids, rail systems, and industrial equipment monitoring. The results indicate that the iceberg representation offers a promising foundation for enhancing explainability, situational awareness, and decision support in the monitoring of complex engineering systems. Full article

This study investigates regional heterogeneity and spatial interdependence in digital skills mismatch across Indonesia by constructing a Digital Skills Supply–Demand Ratio (DSSDR) from the Indonesia Digital Society Index (IMDI). In line with SDG 10 (Reduced Inequalities) and SDG 4 (Quality Education), the study aims to provide policy-relevant evidence to support a more inclusive and balanced digital transformation. Using district-level data and spatial econometric models (OLS, SAR, and the SDM), the analysis evaluates both local determinants and cross-regional spillover effects. Model comparison identifies the Spatial Durbin Model as the best specification, revealing strong spatial dependence in digital skills imbalance. The results show that most local socioeconomic and digital readiness indicators do not have significant direct effects on DSSDR, while school internet coverage exhibits a consistently negative association, indicating that digital demand expands faster than local supply. In contrast, spatial spillovers are decisive: a higher share of ICT study programs in neighboring regions improves local DSSDR through knowledge and human-capital diffusion, whereas higher GRDP per capita in adjacent regions exacerbates local mismatch, consistent with a talent-attraction mechanism. These findings demonstrate that digital skills mismatch is a spatially interconnected phenomenon driven more by interregional dynamics than by local conditions alone, implying that policy responses should move beyond isolated district-level interventions toward coordinated regional strategies integrating education systems, labor markets, and digital ecosystem development. The study contributes a spatially explicit, supply–demand-based framework for diagnosing regional digital inequality and supporting more equitable and sustainable digital development in Indonesia. Full article

Background/Objectives: Geographic Information Systems (GIS) offer essential capabilities for identifying spatial concentrations of vulnerability and strengthening context-aware prevention strategies. This manuscript describes a geospatial architecture designed to generate anticipatory, place-based risk identification applicable across diverse community and institutional environments. Interpersonal Violence (IPV), one of several preventable harms that benefit from this spatially informed analysis, remains a critical public health challenge shaped by structural, ecological, and situational factors. Methods: The conceptual framework presented integrates de-identified surveillance data, ecological indicators, environmental and temporal dynamics into a unified spatial epidemiological model. Multilevel data layers are geocoded, spatially matched, and analyzed using clustering (e.g., Getis-Ord Gi*), spatial dependence metrics (e.g., Moran’s I), and contextual modeling to support anticipatory identification of elevated vulnerability. Framework Outputs: The model is designed to identify spatial clustering, mobility-linked risk patterns, and emerging escalation zones using neighborhood disadvantage, built-environment factors, and situational markers. Outputs are intended to support both clinical decision-making (e.g., geocoded trauma screening, and context-aware discharge planning), and community-level prevention (e.g., targeted environmental interventions and cross-sector resource coordination). Conclusions: This framework synthesizes behavioral theory, spatial epidemiology, and prevention science into an integrative architecture for coordinated public health response. As a conceptual foundation for future empirical research, it advances the development of more dynamic, spatially informed, and equity-focused prevention systems. Full article

The aim of this article is to present the social–ecological system (SES) as a unit of analysis for sustainable water management under conditions of anthropogenic pressure in Poland. In the face of accelerating climate change and growing human impacts, Polish water systems are exposed to increasing ecological stress and to material and immaterial losses affecting local communities. The SES approach provides an integrative analytical framework that links ecological and social components, enabling a holistic view of adaptive and governance processes at multiple spatial scales, from municipalities to areas that transcend administrative boundaries. Methodologically, this study triangulates three complementary approaches to strengthen explanatory inference. This conceptual SES review defines the analytical categories used in the paper, the bibliometric mapping (Scopus database with VOSviewer) identifies dominant research streams and underexplored themes, and the qualitative Polish case studies operationalize these categories to diagnose mechanisms, feedbacks, and governance vulnerabilities under anthropogenic pressure. The bibliometric analysis identifies the main research streams at the intersection of SES, water management and sustainable development, revealing thematic clusters related to climate change adaptation, environmental governance, ecosystem services and hydrological extremes. The case studies - the 2024 flood, the 2022 ecological disaster in the Odra River, and water deficits associated with lignite opencast mining in Eastern Wielkopolska - illustrate how anthropogenic pressure and climate-related hazards interact within local SES and expose governance gaps. Particular attention is paid to attitudes and social participation, understood as configurations of behaviors, knowledge and emotions that shape decision-making in local self-government, especially at the municipal level. This study argues that an SES-based perspective can contribute to building the resilience of water systems, improving the integration of ecological and social dimensions and supporting more sustainable water management in Poland. Full article